{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8e860ec-3257-4e94-b601-53fd668eabf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "案例1 ：不同行业工作年限与收入的线性回归模型\n",
    "案例背景\n",
    "通常来说，收入都会随着工作年限的增长而增长，而在不同的行业中收入的增长速度都会有所不同，\n",
    "本小节就是来通过一元线性回归模型来探寻工作年限对收入的影响，也即搭建收入预测模型，同时比较多个行业的收入预测模型来分析各个行业的特点。\n",
    "\n",
    "这里我将会使用五种不同的模型，普通线性回归，岭回归，lasson回归，弹性网络和多项式回归，\n",
    "并且使用不同的模型进行预测并进行数据的可视化，另外还会对不同的模型之间进行对比分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bca9f76e-809b-407a-90fb-c25335566053",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始分析不同行业工作年限与收入的关系...\n",
      "\n",
      "IT行业分析结果:\n",
      "------------------------------\n",
      "线性回归: R²=0.9969, RMSE=1083.13\n",
      "岭回归: R²=0.9966, RMSE=1124.29\n",
      "Lasso回归: R²=0.9969, RMSE=1083.15\n",
      "弹性网络: R²=0.9967, RMSE=1116.06\n",
      "多项式回归: R²=0.9965, RMSE=1139.46\n",
      "\n",
      "金融行业分析结果:\n",
      "------------------------------\n",
      "线性回归: R²=0.9894, RMSE=2230.34\n",
      "岭回归: R²=0.9899, RMSE=2180.45\n",
      "Lasso回归: R²=0.9894, RMSE=2230.31\n",
      "弹性网络: R²=0.9898, RMSE=2190.36\n",
      "多项式回归: R²=0.9859, RMSE=2568.00\n",
      "\n",
      "汽车制造分析结果:\n",
      "------------------------------\n",
      "线性回归: R²=0.9975, RMSE=780.69\n",
      "岭回归: R²=0.9976, RMSE=752.77\n",
      "Lasso回归: R²=0.9975, RMSE=780.67\n",
      "弹性网络: R²=0.9976, RMSE=758.25\n",
      "多项式回归: R²=0.9980, RMSE=687.90\n",
      "\n",
      "餐饮服务分析结果:\n",
      "------------------------------\n",
      "线性回归: R²=0.9990, RMSE=374.48\n",
      "岭回归: R²=0.9989, RMSE=397.85\n",
      "Lasso回归: R²=0.9990, RMSE=374.50\n",
      "弹性网络: R²=0.9989, RMSE=393.15\n",
      "多项式回归: R²=0.9989, RMSE=383.32\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "模型性能汇总:\n",
      "  行业      模型     R²得分        RMSE\n",
      "IT行业    线性回归 0.996866 1083.125053\n",
      "IT行业     岭回归 0.996623 1124.287082\n",
      "IT行业 Lasso回归 0.996865 1083.153003\n",
      "IT行业    弹性网络 0.996672 1116.060858\n",
      "IT行业   多项式回归 0.996531 1139.464715\n",
      "金融行业    线性回归 0.989399 2230.337982\n",
      "金融行业     岭回归 0.989868 2180.445704\n",
      "金融行业 Lasso回归 0.989399 2230.311547\n",
      "金融行业    弹性网络 0.989776 2190.359395\n",
      "金融行业   多项式回归 0.985946 2567.998000\n",
      "汽车制造    线性回归 0.997459  780.690674\n",
      "汽车制造     岭回归 0.997638  752.770544\n",
      "汽车制造 Lasso回归 0.997460  780.668443\n",
      "汽车制造    弹性网络 0.997603  758.253337\n",
      "汽车制造   多项式回归 0.998027  687.902327\n",
      "餐饮服务    线性回归 0.998996  374.477151\n",
      "餐饮服务     岭回归 0.998867  397.849169\n",
      "餐饮服务 Lasso回归 0.998996  374.502148\n",
      "餐饮服务    弹性网络 0.998894  393.145183\n",
      "餐饮服务   多项式回归 0.998948  383.316616\n",
      "\n",
      "各行业10年工作经验收入预测:\n",
      "----------------------------------------\n",
      "IT行业: 32898.23元 (线性回归)\n",
      "金融行业: 39657.35元 (岭回归)\n",
      "汽车制造: 26798.63元 (多项式回归)\n",
      "餐饮服务: 20002.73元 (线性回归)\n",
      "\n",
      "各行业收入增长率比较:\n",
      "------------------------------\n",
      "IT行业: 年增长2353.23元 (6.91%)\n",
      "金融行业: 年增长3039.17元 (7.39%)\n",
      "汽车制造: 年增长2049.18元 (7.32%)\n",
      "餐饮服务: 年增长1477.95元 (7.13%)\n",
      "\n",
      "分析完成！结果已保存到 '行业收入分析结果.xlsx'\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "# 设置中文字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "def create_sample_data():\n",
    "    \"\"\"创建示例数据\"\"\"\n",
    "    np.random.seed(42)\n",
    "    industries = ['IT行业', '金融行业', '汽车制造', '餐饮服务']\n",
    "    datasets = {}\n",
    "    for industry in industries:\n",
    "        work_years = np.arange(1, 21)\n",
    "        if industry == 'IT行业':\n",
    "            income = 8000 + 2500 * work_years + np.random.normal(0, 1000, 20)\n",
    "        elif industry == '金融行业':\n",
    "            income = 10000 + 3000 * work_years + np.random.normal(0, 1500, 20)\n",
    "        elif industry == '汽车制造':\n",
    "            income = 7000 + 2000 * work_years + np.random.normal(0, 800, 20)\n",
    "        else:\n",
    "            income = 5000 + 1500 * work_years + np.random.normal(0, 500, 20)\n",
    "        datasets[industry] = pd.DataFrame({\n",
    "            '工作年限': work_years,\n",
    "            '收入': income\n",
    "        })\n",
    "    return datasets\n",
    "def get_models():\n",
    "    \"\"\"定义五种回归模型\"\"\"\n",
    "    return {\n",
    "        '线性回归': LinearRegression(),\n",
    "        '岭回归': Ridge(alpha=1.0),\n",
    "        'Lasso回归': Lasso(alpha=0.1, max_iter=10000),\n",
    "        '弹性网络': ElasticNet(alpha=0.1, l1_ratio=0.5, max_iter=10000),\n",
    "        '多项式回归': Pipeline([('poly', PolynomialFeatures(degree=2)), ('linear', LinearRegression())])\n",
    "    }\n",
    "def evaluate_model(model, X_test, y_test):\n",
    "    \"\"\"评估模型性能\"\"\"\n",
    "    y_pred = model.predict(X_test)\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "    rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "    return r2, rmse\n",
    "\n",
    "def analyze_industry(datasets):\n",
    "    \"\"\"分析所有行业\"\"\"\n",
    "    results = {}\n",
    "    for industry, df in datasets.items():\n",
    "        print(f\"\\n{industry}分析结果:\")\n",
    "        print(\"-\" * 30)\n",
    "        X = df[['工作年限']]\n",
    "        y = df['收入']\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "        industry_results = {}\n",
    "        models = get_models()\n",
    "        for name, model in models.items():\n",
    "            model.fit(X_train, y_train)\n",
    "            r2, rmse = evaluate_model(model, X_test, y_test)\n",
    "            industry_results[name] = {'model': model, 'r2': r2, 'rmse': rmse}\n",
    "            print(f\"{name}: R²={r2:.4f}, RMSE={rmse:.2f}\")\n",
    "        \n",
    "        results[industry] = industry_results\n",
    "    return results\n",
    "def plot_comparison(datasets, results):\n",
    "    \"\"\"绘制比较图表\"\"\"\n",
    "    fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n",
    "    axes = axes.ravel()\n",
    "    for idx, (industry, df) in enumerate(datasets.items()):\n",
    "        X = df[['工作年限']]\n",
    "        y = df['收入']\n",
    "        # 绘制散点图\n",
    "        axes[idx].scatter(X, y, alpha=0.6, s=50, label='实际数据')\n",
    "        # 绘制拟合曲线\n",
    "        x_range = np.linspace(X.min().iloc[0], X.max().iloc[0], 100).reshape(-1, 1)\n",
    "        for model_name, result in results[industry].items():\n",
    "            y_pred = result['model'].predict(x_range)\n",
    "            axes[idx].plot(x_range, y_pred, label=model_name, linewidth=2)\n",
    "        \n",
    "        axes[idx].set_title(f'{industry}', fontsize=14)\n",
    "        axes[idx].set_xlabel('工作年限(年)')\n",
    "        axes[idx].set_ylabel('收入(元)')\n",
    "        axes[idx].legend()\n",
    "        axes[idx].grid(True, alpha=0.3)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "def create_summary_table(results):\n",
    "    \"\"\"创建汇总表格\"\"\"\n",
    "    summary_data = []\n",
    "    for industry, models in results.items():\n",
    "        for model_name, result in models.items():\n",
    "            summary_data.append({\n",
    "                '行业': industry,\n",
    "                '模型': model_name,\n",
    "                'R²得分': result['r2'],\n",
    "                'RMSE': result['rmse']\n",
    "            })\n",
    "    \n",
    "    return pd.DataFrame(summary_data)\n",
    "\n",
    "def predict_incomes(results, years=10):\n",
    "    \"\"\"预测各行业收入\"\"\"\n",
    "    print(f\"\\n各行业{years}年工作经验收入预测:\")\n",
    "    print(\"-\" * 40)\n",
    "    \n",
    "    predictions = []\n",
    "    for industry, models in results.items():\n",
    "        # 选择最佳模型\n",
    "        best_model_name = max(models.items(), key=lambda x: x[1]['r2'])[0]\n",
    "        best_model = models[best_model_name]['model']\n",
    "        \n",
    "        # 预测收入\n",
    "        predicted_income = best_model.predict([[years]])[0]\n",
    "        predictions.append({\n",
    "            '行业': industry,\n",
    "            '工作年限': years,\n",
    "            '预测收入': f\"{predicted_income:.2f}元\",\n",
    "            '最佳模型': best_model_name\n",
    "        })\n",
    "        \n",
    "        print(f\"{industry}: {predicted_income:.2f}元 ({best_model_name})\")\n",
    "    return pd.DataFrame(predictions)\n",
    "def compare_growth_rates(results, datasets):\n",
    "    \"\"\"比较各行业增长率\"\"\"\n",
    "    print(\"\\n各行业收入增长率比较:\")\n",
    "    print(\"-\" * 30)\n",
    "    growth_rates = []\n",
    "    for industry, df in datasets.items():\n",
    "        # 使用线性回归系数作为增长率\n",
    "        linear_model = results[industry]['线性回归']['model']\n",
    "        growth_rate = linear_model.coef_[0]\n",
    "        avg_income = df['收入'].mean()\n",
    "        growth_pct = (growth_rate / avg_income) * 100\n",
    "        \n",
    "        growth_rates.append({\n",
    "            '行业': industry,\n",
    "            '年增长率(元)': f\"{growth_rate:.2f}\",\n",
    "            '年增长率(%)': f\"{growth_pct:.2f}%\",\n",
    "            '平均收入': f\"{avg_income:.2f}元\"\n",
    "        }) \n",
    "        print(f\"{industry}: 年增长{growth_rate:.2f}元 ({growth_pct:.2f}%)\")\n",
    "    return pd.DataFrame(growth_rates)\n",
    "\n",
    "# 主程序\n",
    "def main():\n",
    "    print(\"开始分析不同行业工作年限与收入的关系...\")\n",
    "    # 1. 创建数据\n",
    "    datasets = create_sample_data()\n",
    "    # 2. 分析模型\n",
    "    results = analyze_industry(datasets)\n",
    "    # 3. 可视化\n",
    "    plot_comparison(datasets, results)\n",
    "    # 4. 创建汇总表\n",
    "    summary_df = create_summary_table(results)\n",
    "    print(\"\\n模型性能汇总:\")\n",
    "    print(summary_df.to_string(index=False))\n",
    "    # 5. 预测收入\n",
    "    predict_df = predict_incomes(results, years=10)\n",
    "    # 6. 比较增长率\n",
    "    growth_df = compare_growth_rates(results, datasets)\n",
    "    # 7. 保存结果\n",
    "    with pd.ExcelWriter('行业收入分析结果.xlsx') as writer:\n",
    "        for industry, df in datasets.items():\n",
    "            df.to_excel(writer, sheet_name=f'{industry}数据', index=False)\n",
    "        summary_df.to_excel(writer, sheet_name='模型比较', index=False)\n",
    "        predict_df.to_excel(writer, sheet_name='收入预测', index=False)\n",
    "        growth_df.to_excel(writer, sheet_name='增长率比较', index=False)\n",
    "    print(\"\\n分析完成！结果已保存到 '行业收入分析结果.xlsx'\")\n",
    "    return datasets, results, summary_df, predict_df, growth_df\n",
    "# 执行分析\n",
    "if __name__ == \"__main__\":\n",
    "    datasets, results, summary_df, predict_df, growth_df = main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6254d6b3-a275-4ac9-b38d-711cceda9745",
   "metadata": {},
   "outputs": [],
   "source": [
    "因此可以明确看出，金融行业收入年增长率最高，并且10年工作经验的收入也是最高\n",
    "各行业特点分析\n",
    "1. 金融行业 - 高收入领军者\n",
    "收入水平：最高（39,657元）                 增长率：最高（3,039元/年，7.39%）\n",
    "特点分析：\n",
    "典型的\"高起点、高增长\"行业                 专业壁垒高，经验积累价值大\n",
    "可能涉及投资、风险管理等高附加值工作 \n",
    "10年工作经验收入接近4万元，显示强劲的薪资上升空间\n",
    "\n",
    "2. IT行业 - 稳健的技术型行业\n",
    "收入水平：第二高（32,898元）               增长率：中等偏上（2,353元/年，6.91%）\n",
    "特点分析：\n",
    "技术驱动型行业，技能更新快                 增长率相对稳定但略低于金融业\n",
    "反映技术人才的市场价值持续走高\n",
    "工作10年收入超3万元，体现技术经验的溢价\n",
    "\n",
    "3. 汽车制造 - 稳定的传统制造业\n",
    "收入水平：第三（26,799元）                 增长率：较高（2,049元/年，7.32%）\n",
    "特点分析：\n",
    "传统制造业代表，增长率意外较高（7.32%）    可能反映制造业转型升级带来的薪资提升\n",
    "使用多项式回归模型，说明收入增长可能存在非线性特征\n",
    "技术工人和经验工程师的价值得到体现\n",
    "\n",
    "4. 餐饮服务 - 基础服务业\n",
    "收入水平：最低（20,003元）                 增长率：最低（1,478元/年，7.13%）\n",
    "特点分析：\n",
    "入门门槛相对较低，收入基数小               增长率绝对值最低，但百分比并不低（7.13%）\n",
    "反映服务业薪资增长相对平缓\n",
    "10年工作经验收入刚达2万元，显示行业薪资天花板较低"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97cbbeef-0543-4bf0-a594-0747a00541d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "案例2：客户价值预测模型\n",
    "案例背景\n",
    "这里以信用卡客户的客户价值来解释下客户价值预测的具体含义：\n",
    "客户价值预测就是指客户未来一段时间能带来多少利润，其利润的来源可能来自于信用卡的年费、取现手续费、分期手续费、境外交易手续费用等。\n",
    "而分析出客户的价值后，在进行营销、电话接听、催收、产品咨询等各项服务时，就可以针对高价值的客户进行区别于普通客户的服务，\n",
    "有助于进一步挖掘这些高价值客户的价值，并提高这些高价值客户的忠诚度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "320c887c-df8d-4e40-a46b-ea1f5dd1ad7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "客户价值预测模型分析\n",
      "============================================================\n",
      "成功读取客户价值数据表\n",
      "数据形状: (128, 6)\n",
      "实际列名: ['客户价值', '历史贷款金额', '贷款次数', '学历', '月收入', '性别']\n",
      "\n",
      "数据预览:\n",
      "   客户价值  历史贷款金额  贷款次数  学历    月收入  性别\n",
      "0  1150    6488     2   2   9567   1\n",
      "1  1157    5194     4   2  10767   0\n",
      "2  1163    7066     3   2   9317   0\n",
      "3   983    3550     3   2  10517   0\n",
      "4  1205    7847     3   3  11267   1\n",
      "\n",
      "数据列名: ['客户价值', '历史贷款金额', '贷款次数', '学历', '月收入', '性别']\n",
      "使用目标变量: 客户价值\n",
      "\n",
      "特征数量: 5\n",
      "样本数量: 128\n",
      "特征名称: ['历史贷款金额', '贷款次数', '学历', '月收入', '性别']\n",
      "\n",
      "模型性能比较\n",
      "--------------------------------------------------\n",
      "\n",
      "训练线性回归...\n",
      "线性回归........ R²: 0.5803 | RMSE: 156.64 | MAE: 129.75\n",
      "\n",
      "训练岭回归...\n",
      "岭回归......... R²: 0.5827 | RMSE: 156.19 | MAE: 129.36\n",
      "\n",
      "训练Lasso回归...\n",
      "Lasso回归..... R²: 0.5807 | RMSE: 156.56 | MAE: 129.70\n",
      "\n",
      "训练随机森林...\n",
      "随机森林........ R²: 0.6424 | RMSE: 144.57 | MAE: 121.57\n",
      "\n",
      "训练梯度提升...\n",
      "梯度提升........ R²: 0.5433 | RMSE: 163.39 | MAE: 126.87\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "特征重要性分析\n",
      "------------------------------\n",
      "随机森林特征重要性排序:\n",
      "    特征      重要性\n",
      "历史贷款金额 0.387504\n",
      "   月收入 0.357606\n",
      "  贷款次数 0.117675\n",
      "    学历 0.106706\n",
      "    性别 0.030509\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "客户价值分群分析\n",
      "------------------------------\n",
      "客户价值分群统计:\n",
      "         预测价值         历史贷款金额  贷款次数\n",
      "         mean count               \n",
      "价值等级                              \n",
      "低价值   1061.33    32  5053.94  2.59\n",
      "中价值   1226.15    32  6368.41  2.72\n",
      "高价值   1377.66    32  6275.84  3.09\n",
      "超高价值  1641.30    32  7975.94  3.69\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "业务建议:\n",
      "1. 最佳预测模型: 随机森林\n",
      "2. 高价值客户占比: 25.0%\n",
      "3. 平均客户价值: 1326.61\n",
      "\n",
      "分析结果已保存到 '客户价值预测分析结果.xlsx'\n",
      "\n",
      "实际特征名称: ['历史贷款金额', '贷款次数', '学历', '月收入', '性别']\n",
      "特征数量: 5\n",
      "示例客户数据: [0, 80, 0, 50.0, 0]\n",
      "\n",
      "示例客户预测价值: 950.26\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# 设置中文字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "def load_customer_data():\n",
    "    \"\"\"加载客户价值数据\"\"\"\n",
    "    try:\n",
    "        file_path = r\"d:\\桌面\\商业数据分析\\@Python大数据分析与机器学习商业案例实战\\第3章 线性回归模型\\源代码汇总_Jupyter Notebook格式（推荐）\\客户价值数据表.xlsx\"\n",
    "        df = pd.read_excel(file_path)\n",
    "        print(\"成功读取客户价值数据表\")\n",
    "        print(f\"数据形状: {df.shape}\")\n",
    "        print(f\"实际列名: {df.columns.tolist()}\")\n",
    "        return df\n",
    "    except Exception as e:\n",
    "        print(f\"读取文件失败: {e}\")\n",
    "        print(\"使用模拟数据进行分析...\")\n",
    "        return generate_sample_data()\n",
    "\n",
    "def generate_sample_data():\n",
    "    \"\"\"生成模拟的客户价值数据\"\"\"\n",
    "    np.random.seed(42)\n",
    "    n_samples = 1000\n",
    "    \n",
    "    data = {\n",
    "        '年龄': np.random.randint(25, 65, n_samples),\n",
    "        '年收入_万元': np.random.uniform(10, 100, n_samples),\n",
    "        '信用卡额度_万': np.random.uniform(1, 20, n_samples),\n",
    "        '月均消费_元': np.random.uniform(1000, 20000, n_samples),\n",
    "        '信用评分': np.random.randint(300, 850, n_samples),\n",
    "        '持卡时长_月': np.random.randint(6, 120, n_samples),\n",
    "        '近半年交易次数': np.random.randint(10, 200, n_samples),\n",
    "        '是否VIP': np.random.choice([0, 1], n_samples, p=[0.7, 0.3]),\n",
    "    }\n",
    "    \n",
    "    df = pd.DataFrame(data)\n",
    "    \n",
    "    # 生成客户价值\n",
    "    df['客户价值_年利润_元'] = (\n",
    "        500 +\n",
    "        df['年收入_万元'] * 80 +\n",
    "        df['信用卡额度_万'] * 50 +\n",
    "        df['月均消费_元'] * 0.1 +\n",
    "        df['信用评分'] * 2 +\n",
    "        df['持卡时长_月'] * 3 +\n",
    "        df['近半年交易次数'] * 5 +\n",
    "        df['是否VIP'] * 1000 +\n",
    "        np.random.normal(0, 500, n_samples)\n",
    "    )\n",
    "    \n",
    "    return df\n",
    "\n",
    "def get_regression_models():\n",
    "    \"\"\"定义五种回归模型\"\"\"\n",
    "    models = {\n",
    "        '线性回归': LinearRegression(),\n",
    "        '岭回归': Ridge(alpha=1.0),\n",
    "        'Lasso回归': Lasso(alpha=0.1, max_iter=10000),\n",
    "        '随机森林': RandomForestRegressor(n_estimators=100, random_state=42),\n",
    "        '梯度提升': GradientBoostingRegressor(n_estimators=100, random_state=42)\n",
    "    }\n",
    "    return models\n",
    "\n",
    "def evaluate_model(model, X_test, y_test):\n",
    "    \"\"\"评估模型性能\"\"\"\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    metrics = {\n",
    "        'R²': r2_score(y_test, y_pred),\n",
    "        'RMSE': np.sqrt(mean_squared_error(y_test, y_pred)),\n",
    "        'MAE': mean_absolute_error(y_test, y_pred),\n",
    "    }\n",
    "    \n",
    "    # 计算MAPE（避免除零错误）\n",
    "    try:\n",
    "        mape = np.mean(np.abs((y_test - y_pred) / np.where(y_test == 0, 1, y_test))) * 100\n",
    "        metrics['MAPE'] = mape\n",
    "    except:\n",
    "        metrics['MAPE'] = np.nan\n",
    "    \n",
    "    return metrics, y_pred\n",
    "\n",
    "def analyze_customer_value():\n",
    "    \"\"\"主分析函数\"\"\"\n",
    "    print(\"=\" * 60)\n",
    "    print(\"客户价值预测模型分析\")\n",
    "    print(\"=\" * 60)\n",
    "    \n",
    "    # 1. 加载数据\n",
    "    df = load_customer_data()\n",
    "    print(f\"\\n数据预览:\")\n",
    "    print(df.head())\n",
    "    print(f\"\\n数据列名: {df.columns.tolist()}\")\n",
    "    \n",
    "    # 2. 自动识别目标变量\n",
    "    target_keywords = ['价值', '利润', '收入', 'profit', 'value', 'revenue', 'income']\n",
    "    target_column = None\n",
    "    \n",
    "    for col in df.columns:\n",
    "        for keyword in target_keywords:\n",
    "            if keyword in col:\n",
    "                target_column = col\n",
    "                break\n",
    "        if target_column:\n",
    "            break\n",
    "    \n",
    "    # 如果没有找到合适的目标变量，使用最后一列或创建模拟目标变量\n",
    "    if target_column is None:\n",
    "        if len(df.columns) > 1:\n",
    "            target_column = df.columns[-1]\n",
    "            print(f\"未找到明确的目标变量列，使用最后一列: {target_column}\")\n",
    "        else:\n",
    "            print(\"数据只有一列，创建模拟目标变量\")\n",
    "            df = generate_sample_data()\n",
    "            target_column = '客户价值_年利润_元'\n",
    "    \n",
    "    print(f\"使用目标变量: {target_column}\")\n",
    "    \n",
    "    # 3. 数据预处理\n",
    "    X = df.drop(target_column, axis=1)\n",
    "    y = df[target_column]\n",
    "    \n",
    "    print(f\"\\n特征数量: {X.shape[1]}\")\n",
    "    print(f\"样本数量: {X.shape[0]}\")\n",
    "    print(f\"特征名称: {X.columns.tolist()}\")\n",
    "    \n",
    "    # 检查并处理非数值型数据\n",
    "    non_numeric_cols = X.select_dtypes(include=['object']).columns\n",
    "    if len(non_numeric_cols) > 0:\n",
    "        print(f\"发现非数值型列: {non_numeric_cols.tolist()}\")\n",
    "        for col in non_numeric_cols:\n",
    "            if X[col].nunique() <= 10:\n",
    "                X[col] = X[col].astype('category').cat.codes\n",
    "            else:\n",
    "                X = X.drop(col, axis=1)\n",
    "                print(f\"删除列: {col}\")\n",
    "    \n",
    "    # 划分训练集和测试集\n",
    "    X_train, X_test, y_train, y_test = train_test_split(\n",
    "        X, y, test_size=0.2, random_state=42\n",
    "    )\n",
    "    \n",
    "    # 4. 模型训练和评估\n",
    "    models = get_regression_models()\n",
    "    results = {}\n",
    "    feature_names = X.columns.tolist()  # 保存特征名称\n",
    "    \n",
    "    print(f\"\\n模型性能比较\")\n",
    "    print(\"-\" * 50)\n",
    "    \n",
    "    for name, model in models.items():\n",
    "        print(f\"\\n训练{name}...\")\n",
    "        \n",
    "        try:\n",
    "            model.fit(X_train, y_train)\n",
    "            metrics, y_pred = evaluate_model(model, X_test, y_test)\n",
    "            cv_scores = cross_val_score(model, X, y, cv=5, scoring='r2')\n",
    "            \n",
    "            results[name] = {\n",
    "                'model': model,\n",
    "                'metrics': metrics,\n",
    "                'predictions': y_pred,\n",
    "                'cv_mean': cv_scores.mean(),\n",
    "                'feature_names': feature_names  # 保存特征名称\n",
    "            }\n",
    "            \n",
    "            print(f\"{name:.<12} R²: {metrics['R²']:.4f} | RMSE: {metrics['RMSE']:.2f} | MAE: {metrics['MAE']:.2f}\")\n",
    "        except Exception as e:\n",
    "            print(f\"{name} 训练失败: {e}\")\n",
    "            results[name] = None\n",
    "    \n",
    "    # 5. 可视化结果\n",
    "    plot_results(results, y_test, df, target_column)\n",
    "    \n",
    "    # 6. 特征重要性分析\n",
    "    analyze_feature_importance(results, feature_names)\n",
    "    \n",
    "    # 7. 客户分群分析\n",
    "    customer_segmentation_analysis(results, df, X, target_column)\n",
    "    \n",
    "    return df, results, target_column, feature_names\n",
    "\n",
    "def plot_results(results, y_test, df, target_column):\n",
    "    \"\"\"绘制结果可视化\"\"\"\n",
    "    valid_results = {k: v for k, v in results.items() if v is not None}\n",
    "    \n",
    "    if not valid_results:\n",
    "        print(\"没有有效的模型结果可可视化\")\n",
    "        return\n",
    "    \n",
    "    plt.figure(figsize=(15, 5))\n",
    "    \n",
    "    # 模型性能比较\n",
    "    plt.subplot(1, 3, 1)\n",
    "    model_names = list(valid_results.keys())\n",
    "    r2_scores = [valid_results[name]['metrics']['R²'] for name in model_names]\n",
    "    plt.bar(model_names, r2_scores, color=['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FECA57'])\n",
    "    plt.title('模型R²得分比较')\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.ylabel('R²得分')\n",
    "    \n",
    "    # 最佳模型预测效果\n",
    "    plt.subplot(1, 3, 2)\n",
    "    best_model_name = max(valid_results.items(), key=lambda x: x[1]['metrics']['R²'])[0]\n",
    "    best_predictions = valid_results[best_model_name]['predictions']\n",
    "    plt.scatter(y_test, best_predictions, alpha=0.6)\n",
    "    plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--', lw=2)\n",
    "    plt.xlabel('实际客户价值')\n",
    "    plt.ylabel('预测客户价值')\n",
    "    plt.title(f'最佳模型({best_model_name})预测效果')\n",
    "    \n",
    "    # 客户价值分布\n",
    "    plt.subplot(1, 3, 3)\n",
    "    plt.hist(df[target_column], bins=30, alpha=0.7, color='#45B7D1', edgecolor='black')\n",
    "    plt.xlabel('客户价值')\n",
    "    plt.ylabel('客户数量')\n",
    "    plt.title('客户价值分布')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "def analyze_feature_importance(results, feature_names):\n",
    "    \"\"\"分析特征重要性\"\"\"\n",
    "    print(f\"\\n特征重要性分析\")\n",
    "    print(\"-\" * 30)\n",
    "    \n",
    "    if '随机森林' in results and results['随机森林'] is not None:\n",
    "        rf_model = results['随机森林']['model']\n",
    "        importance = rf_model.feature_importances_\n",
    "        \n",
    "        feature_imp_df = pd.DataFrame({\n",
    "            '特征': feature_names,\n",
    "            '重要性': importance\n",
    "        }).sort_values('重要性', ascending=False)\n",
    "        \n",
    "        print(\"随机森林特征重要性排序:\")\n",
    "        print(feature_imp_df.to_string(index=False))\n",
    "        \n",
    "        plt.figure(figsize=(10, 6))\n",
    "        sns.barplot(data=feature_imp_df.head(10), x='重要性', y='特征', palette='viridis')\n",
    "        plt.title('特征重要性分析(随机森林)')\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "def customer_segmentation_analysis(results, df, X, target_column):\n",
    "    \"\"\"客户分群和价值预测分析\"\"\"\n",
    "    print(f\"\\n客户价值分群分析\")\n",
    "    print(\"-\" * 30)\n",
    "    \n",
    "    valid_results = {k: v for k, v in results.items() if v is not None}\n",
    "    if not valid_results:\n",
    "        print(\"没有有效的模型进行分群分析\")\n",
    "        return\n",
    "    \n",
    "    best_model_name = max(valid_results.items(), key=lambda x: x[1]['metrics']['R²'])[0]\n",
    "    best_model = valid_results[best_model_name]['model']\n",
    "    \n",
    "    df['预测价值'] = best_model.predict(X)\n",
    "    df['价值等级'] = pd.qcut(df['预测价值'], q=4, labels=['低价值', '中价值', '高价值', '超高价值'])\n",
    "    \n",
    "    # 分群统计\n",
    "    segment_stats = df.groupby('价值等级').agg({\n",
    "        '预测价值': ['mean', 'count'],\n",
    "    }).round(2)\n",
    "    \n",
    "    numeric_cols = df.select_dtypes(include=[np.number]).columns\n",
    "    for col in numeric_cols[:3]:\n",
    "        if col != '预测价值' and col != target_column:\n",
    "            stats = df.groupby('价值等级')[col].mean().round(2)\n",
    "            segment_stats[col] = stats\n",
    "    \n",
    "    print(\"客户价值分群统计:\")\n",
    "    print(segment_stats)\n",
    "    \n",
    "    plt.figure(figsize=(12, 4))\n",
    "    \n",
    "    plt.subplot(1, 2, 1)\n",
    "    segment_counts = df['价值等级'].value_counts()\n",
    "    plt.pie(segment_counts.values, labels=segment_counts.index, autopct='%1.1f%%')\n",
    "    plt.title('客户价值等级分布')\n",
    "    \n",
    "    plt.subplot(1, 2, 2)\n",
    "    df.boxplot(column='预测价值', by='价值等级', ax=plt.gca())\n",
    "    plt.title('各价值等级客户价值分布')\n",
    "    plt.suptitle('')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    high_value_customers = df[df['价值等级'] == '超高价值']\n",
    "    print(f\"\\n业务建议:\")\n",
    "    print(f\"1. 最佳预测模型: {best_model_name}\")\n",
    "    print(f\"2. 高价值客户占比: {len(high_value_customers)/len(df)*100:.1f}%\")\n",
    "    print(f\"3. 平均客户价值: {df['预测价值'].mean():.2f}\")\n",
    "\n",
    "def predict_new_customer(model, customer_data, feature_names):\n",
    "    \"\"\"预测新客户的价值\"\"\"\n",
    "    # 确保输入数据的特征顺序与训练时一致\n",
    "    if len(customer_data) != len(feature_names):\n",
    "        print(f\"警告: 输入特征数量({len(customer_data)})与模型期望数量({len(feature_names)})不匹配\")\n",
    "        # 截断或填充特征\n",
    "        if len(customer_data) > len(feature_names):\n",
    "            customer_data = customer_data[:len(feature_names)]\n",
    "            print(f\"截断特征到前{len(feature_names)}个\")\n",
    "        else:\n",
    "            customer_data = customer_data + [0] * (len(feature_names) - len(customer_data))\n",
    "            print(f\"填充特征到{len(feature_names)}个\")\n",
    "    \n",
    "    prediction = model.predict([customer_data])[0]\n",
    "    return prediction\n",
    "\n",
    "def create_example_customer(feature_names):\n",
    "    \"\"\"根据实际特征创建示例客户数据\"\"\"\n",
    "    example_data = {}\n",
    "    \n",
    "    # 为每个特征提供合理的默认值\n",
    "    for feature in feature_names:\n",
    "        if '年龄' in feature:\n",
    "            example_data[feature] = 35\n",
    "        elif '收入' in feature:\n",
    "            example_data[feature] = 50.0\n",
    "        elif '额度' in feature:\n",
    "            example_data[feature] = 8.0\n",
    "        elif '消费' in feature:\n",
    "            example_data[feature] = 8000\n",
    "        elif '信用' in feature or '评分' in feature:\n",
    "            example_data[feature] = 750\n",
    "        elif '时长' in feature or '月' in feature:\n",
    "            example_data[feature] = 24\n",
    "        elif '交易' in feature or '次数' in feature:\n",
    "            example_data[feature] = 80\n",
    "        elif 'VIP' in feature or 'vip' in feature:\n",
    "            example_data[feature] = 0\n",
    "        else:\n",
    "            example_data[feature] = 0  # 默认值\n",
    "    \n",
    "    # 按特征名称顺序返回数值列表\n",
    "    return [example_data[feature] for feature in feature_names]\n",
    "\n",
    "# 执行分析\n",
    "if __name__ == \"__main__\":\n",
    "    df, results, target_column, feature_names = analyze_customer_value()\n",
    "    \n",
    "    # 保存结果\n",
    "    try:\n",
    "        with pd.ExcelWriter('客户价值预测分析结果.xlsx') as writer:\n",
    "            df.to_excel(writer, sheet_name='原始数据', index=False)\n",
    "            \n",
    "            performance_data = []\n",
    "            for model_name, result in results.items():\n",
    "                if result is not None:\n",
    "                    performance_data.append({\n",
    "                        '模型': model_name,\n",
    "                        'R²得分': result['metrics']['R²'],\n",
    "                        'RMSE': result['metrics']['RMSE'],\n",
    "                        'MAE': result['metrics']['MAE'],\n",
    "                        '交叉验证R²': result['cv_mean']\n",
    "                    })\n",
    "            \n",
    "            pd.DataFrame(performance_data).to_excel(writer, sheet_name='模型性能', index=False)\n",
    "        \n",
    "        print(\"\\n分析结果已保存到 '客户价值预测分析结果.xlsx'\")\n",
    "    except Exception as e:\n",
    "        print(f\"保存文件时出错: {e}\")\n",
    "    \n",
    "    # 示例预测\n",
    "    valid_results = {k: v for k, v in results.items() if v is not None}\n",
    "    if valid_results:\n",
    "        best_model_name = max(valid_results.items(), key=lambda x: x[1]['metrics']['R²'])[0]\n",
    "        best_model = valid_results[best_model_name]['model']\n",
    "        \n",
    "        print(f\"\\n实际特征名称: {feature_names}\")\n",
    "        print(f\"特征数量: {len(feature_names)}\")\n",
    "        \n",
    "        # 创建正确的示例客户数据\n",
    "        example_customer = create_example_customer(feature_names)\n",
    "        print(f\"示例客户数据: {example_customer}\")\n",
    "        \n",
    "        predicted_value = predict_new_customer(best_model, example_customer, feature_names)\n",
    "        print(f\"\\n示例客户预测价值: {predicted_value:.2f}\")\n",
    "        \n",
    "        # 显示各特征对预测的贡献（对于线性模型）\n",
    "        if hasattr(best_model, 'coef_'):\n",
    "            print(f\"\\n特征贡献分析:\")\n",
    "            for i, (feature, coef) in enumerate(zip(feature_names, best_model.coef_)):\n",
    "                print(f\"{feature}: {coef:.4f}\")\n",
    "    else:\n",
    "        print(\"没有有效的模型可用于预测\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fc2714f-feea-4f38-82ec-6ec168384fb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "案例3：股票客户流失预警模型\n",
    "案例背景\n",
    "在进行每一笔股票交易的时候，交易者（股民）都是要付给开户所在的证券公司一些手续费的，虽然单笔交易的手续费并不高，\n",
    "然而股票市场每日都有巨额的成交量，使得每一笔交易的手续费汇总起来的数目相当可观，而这一部分收入对于一些证券公司来说很重要，\n",
    "甚至可以占到所有营业收入50%以上，因此证券公司对于客户（也即交易者）的忠诚度和活跃度是很看重的。\n",
    "\n",
    "如果一个客户不再通过该证券公司交易，也即该客户流失了，那么对于证券公司来说便损失了一个收入来源，\n",
    "因此证券公司会搭建一套客户流失预警模型来预测交易者是否会流失，从而对于流失概率较大的客户进行相应的挽回措施，\n",
    "因为通常情况下，获得新客户的成本比保留现有客户的成本要高的多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7007e2d4-5875-492e-9e4a-6383a03672da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "股票客户流失预警模型分析\n",
      "============================================================\n",
      "成功读取股票客户流失数据\n",
      "数据形状: (7043, 6)\n",
      "实际列名: ['账户资金（元）', '最后一次交易距今时间（天）', '上月交易佣金（元）', '累计交易佣金（元）', '本券商使用时长（年）', '是否流失']\n",
      "\n",
      "数据基本信息:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   账户资金（元）        7043 non-null   float64\n",
      " 1   最后一次交易距今时间（天）  7043 non-null   int64  \n",
      " 2   上月交易佣金（元）      7043 non-null   float64\n",
      " 3   累计交易佣金（元）      7043 non-null   float64\n",
      " 4   本券商使用时长（年）     7043 non-null   int64  \n",
      " 5   是否流失           7043 non-null   int64  \n",
      "dtypes: float64(3), int64(3)\n",
      "memory usage: 330.3 KB\n",
      "None\n",
      "\n",
      "描述性统计:\n",
      "             账户资金（元）  最后一次交易距今时间（天）    上月交易佣金（元）     累计交易佣金（元）   本券商使用时长（年）  \\\n",
      "count    7043.000000    7043.000000  7043.000000   7043.000000  7043.000000   \n",
      "mean   225176.087321      94.484453   323.808462   4279.734304     2.077808   \n",
      "std    204011.502272      94.459447   150.450235   2266.794470     1.859040   \n",
      "min     20000.000000       0.000000    91.250000   2000.000000     0.000000   \n",
      "25%     55869.500000      16.000000   177.500000   2398.550000     0.000000   \n",
      "50%    145509.500000      58.000000   351.750000   3394.550000     2.000000   \n",
      "75%    360794.000000     157.000000   449.250000   5786.600000     4.000000   \n",
      "max    801632.000000     361.000000   593.750000  10684.800000     5.000000   \n",
      "\n",
      "              是否流失  \n",
      "count  7043.000000  \n",
      "mean      0.265370  \n",
      "std       0.441561  \n",
      "min       0.000000  \n",
      "25%       0.000000  \n",
      "50%       0.000000  \n",
      "75%       1.000000  \n",
      "max       1.000000  \n",
      "\n",
      "数据预览:\n",
      "    账户资金（元）  最后一次交易距今时间（天）  上月交易佣金（元）  累计交易佣金（元）  本券商使用时长（年）  是否流失\n",
      "0   22686.5            297     149.25    2029.85           0     0\n",
      "1  190055.0             42     284.75    3889.50           2     0\n",
      "2   29733.5            233     269.25    2108.15           0     1\n",
      "3  185667.5             44     211.50    3840.75           3     0\n",
      "4   33648.5            213     353.50    2151.65           0     1\n",
      "使用目标变量: 是否流失\n",
      "流失客户数: 1869\n",
      "流失率: 26.54%\n",
      "\n",
      "特征信息:\n",
      "特征数量: 5\n",
      "特征名称: ['账户资金（元）', '最后一次交易距今时间（天）', '上月交易佣金（元）', '累计交易佣金（元）', '本券商使用时长（年）']\n",
      "\n",
      "==================================================\n",
      "模型性能比较\n",
      "==================================================\n",
      "\n",
      "训练逻辑回归...\n",
      "逻辑回归........ 准确率: 0.7864 | 精确率: 0.6409\n",
      "             召回率: 0.4439 | F1分数: 0.5245\n",
      "             AUC: 0.8158 | 交叉验证F1: 0.5194\n",
      "\n",
      "训练随机森林...\n",
      "随机森林........ 准确率: 0.7530 | 精确率: 0.5464\n",
      "             召回率: 0.4091 | F1分数: 0.4679\n",
      "             AUC: 0.7566 | 交叉验证F1: 0.5018\n",
      "\n",
      "训练梯度提升...\n",
      "梯度提升........ 准确率: 0.7864 | 精确率: 0.6398\n",
      "             召回率: 0.4465 | F1分数: 0.5260\n",
      "             AUC: 0.8165 | 交叉验证F1: 0.5275\n",
      "\n",
      "训练支持向量机...\n",
      "支持向量机....... 准确率: 0.7899 | 精确率: 0.6789\n",
      "             召回率: 0.3957 | F1分数: 0.5000\n",
      "             AUC: 0.7577 | 交叉验证F1: 0.4875\n",
      "\n",
      "训练K近邻...\n",
      "K近邻......... 准确率: 0.7601 | 精确率: 0.5570\n",
      "             召回率: 0.4706 | F1分数: 0.5101\n",
      "             AUC: 0.7478 | 交叉验证F1: 0.5176\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x1200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==================================================\n",
      "特征重要性分析\n",
      "==================================================\n",
      "\n",
      "随机森林特征重要性排序:\n",
      "           特征      重要性\n",
      "    上月交易佣金（元） 0.322618\n",
      "    累计交易佣金（元） 0.231846\n",
      "      账户资金（元） 0.229398\n",
      "最后一次交易距今时间（天） 0.130558\n",
      "   本券商使用时长（年） 0.085581\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==================================================\n",
      "客户风险分群分析\n",
      "==================================================\n",
      "客户风险分群统计:\n",
      "       流失概率         是否流失\n",
      "       mean count   mean\n",
      "风险等级                    \n",
      "低风险   0.119  4507  0.108\n",
      "中风险   0.465  2035  0.477\n",
      "高风险   0.780   501  0.818\n",
      "\n",
      "高风险客户数量: 501\n",
      "高风险客户中实际流失比例: 81.84%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "业务建议:\n",
      "1. 最佳预警模型: 梯度提升\n",
      "2. 高风险客户占比: 7.1%\n",
      "3. 建议重点关注: 高风险客户群体\n",
      "\n",
      "分析结果已保存到 '股票客户流失预警分析结果.xlsx'\n",
      "\n",
      "实际特征名称: Index(['账户资金（元）', '最后一次交易距今时间（天）', '上月交易佣金（元）', '累计交易佣金（元）', '本券商使用时长（年）'], dtype='object')\n",
      "特征数量: 5\n",
      "\n",
      "示例客户流失预测:\n",
      "流失概率: 0.1337\n",
      "风险等级: 低风险\n",
      "建议措施: 正常维护\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix, classification_report\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# 设置中文字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "def load_stock_data():\n",
    "    \"\"\"加载股票客户数据\"\"\"\n",
    "    try:\n",
    "        file_path = r\"d:\\桌面\\商业数据分析\\@Python大数据分析与机器学习商业案例实战\\第4章 逻辑回归模型\\源代码汇总_Jupyter Notebook格式（推荐）\\股票客户流失.xlsx\"\n",
    "        df = pd.read_excel(file_path)\n",
    "        print(\"成功读取股票客户流失数据\")\n",
    "        print(f\"数据形状: {df.shape}\")\n",
    "        print(f\"实际列名: {df.columns.tolist()}\")\n",
    "        \n",
    "        # 显示数据基本信息\n",
    "        print(f\"\\n数据基本信息:\")\n",
    "        print(df.info())\n",
    "        print(f\"\\n描述性统计:\")\n",
    "        print(df.describe())\n",
    "        \n",
    "        return df\n",
    "    except Exception as e:\n",
    "        print(f\"读取文件失败: {e}\")\n",
    "        print(\"使用模拟数据进行分析...\")\n",
    "        return generate_sample_stock_data()\n",
    "\n",
    "def generate_sample_stock_data():\n",
    "    \"\"\"生成模拟的股票客户流失数据\"\"\"\n",
    "    np.random.seed(42)\n",
    "    n_samples = 2000\n",
    "    \n",
    "    data = {\n",
    "        '年龄': np.random.randint(25, 70, n_samples),\n",
    "        '资产规模_万元': np.random.uniform(1, 500, n_samples),\n",
    "        '月交易频率': np.random.randint(1, 50, n_samples),\n",
    "        '平均交易金额_万元': np.random.uniform(0.1, 50, n_samples),\n",
    "        '账户余额_万元': np.random.uniform(0.1, 100, n_samples),\n",
    "        '佣金费率': np.random.uniform(0.01, 0.05, n_samples),\n",
    "        '持有产品数量': np.random.randint(1, 10, n_samples),\n",
    "        '最近登录天数': np.random.randint(1, 90, n_samples),\n",
    "        '客户服务评分': np.random.randint(1, 10, n_samples),\n",
    "        '是否使用移动端': np.random.choice([0, 1], n_samples, p=[0.3, 0.7]),\n",
    "        '是否参与活动': np.random.choice([0, 1], n_samples, p=[0.6, 0.4]),\n",
    "    }\n",
    "    \n",
    "    df = pd.DataFrame(data)\n",
    "    \n",
    "    # 生成流失标签（基于多个特征的逻辑组合）\n",
    "    churn_prob = (\n",
    "        0.1 +\n",
    "        (df['年龄'] > 60) * 0.2 +  # 年龄大的客户更容易流失\n",
    "        (df['资产规模_万元'] < 10) * 0.3 +  # 资产少的客户更容易流失\n",
    "        (df['月交易频率'] < 5) * 0.4 +  # 交易不活跃的客户更容易流失\n",
    "        (df['最近登录天数'] > 30) * 0.5 +  # 长期不登录的客户更容易流失\n",
    "        (df['客户服务评分'] < 3) * 0.3 +  # 服务评分低的客户更容易流失\n",
    "        (df['是否使用移动端'] == 0) * 0.2 +  # 不使用移动端的客户更容易流失\n",
    "        np.random.normal(0, 0.5, n_samples)  # 随机噪声\n",
    "    )\n",
    "    \n",
    "    df['是否流失'] = (churn_prob > 0.5).astype(int)\n",
    "    \n",
    "    print(f\"流失客户比例: {df['是否流失'].mean():.2%}\")\n",
    "    return df\n",
    "\n",
    "def get_classification_models():\n",
    "    \"\"\"定义五种分类模型\"\"\"\n",
    "    models = {\n",
    "        '逻辑回归': LogisticRegression(random_state=42, max_iter=1000),\n",
    "        '随机森林': RandomForestClassifier(n_estimators=100, random_state=42),\n",
    "        '梯度提升': GradientBoostingClassifier(n_estimators=100, random_state=42),\n",
    "        '支持向量机': SVC(probability=True, random_state=42),\n",
    "        'K近邻': KNeighborsClassifier(n_neighbors=5)\n",
    "    }\n",
    "    return models\n",
    "\n",
    "def evaluate_model(model, X_test, y_test):\n",
    "    \"\"\"评估模型性能\"\"\"\n",
    "    y_pred = model.predict(X_test)\n",
    "    y_pred_proba = model.predict_proba(X_test)[:, 1] if hasattr(model, 'predict_proba') else None\n",
    "    \n",
    "    metrics = {\n",
    "        '准确率': accuracy_score(y_test, y_pred),\n",
    "        '精确率': precision_score(y_test, y_pred, zero_division=0),\n",
    "        '召回率': recall_score(y_test, y_pred, zero_division=0),\n",
    "        'F1分数': f1_score(y_test, y_pred, zero_division=0),\n",
    "    }\n",
    "    \n",
    "    if y_pred_proba is not None:\n",
    "        metrics['AUC'] = roc_auc_score(y_test, y_pred_proba)\n",
    "    else:\n",
    "        metrics['AUC'] = None\n",
    "    \n",
    "    return metrics, y_pred, y_pred_proba\n",
    "\n",
    "def analyze_customer_churn():\n",
    "    \"\"\"主分析函数\"\"\"\n",
    "    print(\"=\" * 60)\n",
    "    print(\"股票客户流失预警模型分析\")\n",
    "    print(\"=\" * 60)\n",
    "    \n",
    "    # 1. 加载数据\n",
    "    df = load_stock_data()\n",
    "    print(f\"\\n数据预览:\")\n",
    "    print(df.head())\n",
    "    \n",
    "    # 2. 数据预处理\n",
    "    # 自动识别目标变量\n",
    "    target_keywords = ['流失', 'churn', '是否流失', '是否离开', 'Exited']\n",
    "    target_column = None\n",
    "    \n",
    "    for col in df.columns:\n",
    "        for keyword in target_keywords:\n",
    "            if keyword.lower() in str(col).lower():\n",
    "                target_column = col\n",
    "                break\n",
    "        if target_column:\n",
    "            break\n",
    "    \n",
    "    if target_column is None:\n",
    "        target_column = df.columns[-1]\n",
    "        print(f\"未找到明确的目标变量列，使用最后一列: {target_column}\")\n",
    "    \n",
    "    print(f\"使用目标变量: {target_column}\")\n",
    "    print(f\"流失客户数: {df[target_column].sum()}\")\n",
    "    print(f\"流失率: {df[target_column].mean():.2%}\")\n",
    "    \n",
    "    X = df.drop(target_column, axis=1)\n",
    "    y = df[target_column]\n",
    "    \n",
    "    # 显示特征信息\n",
    "    print(f\"\\n特征信息:\")\n",
    "    print(f\"特征数量: {X.shape[1]}\")\n",
    "    print(f\"特征名称: {X.columns.tolist()}\")\n",
    "    \n",
    "    # 处理非数值型数据\n",
    "    for col in X.select_dtypes(include=['object']).columns:\n",
    "        if X[col].nunique() <= 10:\n",
    "            X[col] = X[col].astype('category').cat.codes\n",
    "            print(f\"编码分类变量: {col}\")\n",
    "        else:\n",
    "            X = X.drop(col, axis=1)\n",
    "            print(f\"删除文本变量: {col}\")\n",
    "    \n",
    "    # 特征标准化\n",
    "    scaler = StandardScaler()\n",
    "    X_scaled = scaler.fit_transform(X)\n",
    "    \n",
    "    # 划分训练集和测试集\n",
    "    X_train, X_test, y_train, y_test = train_test_split(\n",
    "        X_scaled, y, test_size=0.2, random_state=42, stratify=y\n",
    "    )\n",
    "    \n",
    "    # 3. 模型训练和评估\n",
    "    models = get_classification_models()\n",
    "    results = {}\n",
    "    \n",
    "    print(f\"\\n{'='*50}\")\n",
    "    print(\"模型性能比较\")\n",
    "    print(f\"{'='*50}\")\n",
    "    \n",
    "    for name, model in models.items():\n",
    "        print(f\"\\n训练{name}...\")\n",
    "        \n",
    "        try:\n",
    "            model.fit(X_train, y_train)\n",
    "            metrics, y_pred, y_pred_proba = evaluate_model(model, X_test, y_test)\n",
    "            \n",
    "            # 交叉验证\n",
    "            cv_scores = cross_val_score(model, X_scaled, y, cv=5, scoring='f1')\n",
    "            \n",
    "            results[name] = {\n",
    "                'model': model,\n",
    "                'metrics': metrics,\n",
    "                'predictions': y_pred,\n",
    "                'probabilities': y_pred_proba,\n",
    "                'cv_mean': cv_scores.mean(),\n",
    "                'cv_std': cv_scores.std(),\n",
    "                'feature_names': X.columns.tolist()\n",
    "            }\n",
    "            \n",
    "            print(f\"{name:.<12} 准确率: {metrics['准确率']:.4f} | 精确率: {metrics['精确率']:.4f}\")\n",
    "            print(f\"{' ':12} 召回率: {metrics['召回率']:.4f} | F1分数: {metrics['F1分数']:.4f}\")\n",
    "            if metrics['AUC']:\n",
    "                print(f\"{' ':12} AUC: {metrics['AUC']:.4f} | 交叉验证F1: {cv_scores.mean():.4f}\")\n",
    "            \n",
    "        except Exception as e:\n",
    "            print(f\"{name} 训练失败: {e}\")\n",
    "            results[name] = None\n",
    "    \n",
    "    # 4. 可视化结果\n",
    "    plot_results(results, y_test, df, target_column)\n",
    "    \n",
    "    # 5. 特征重要性分析\n",
    "    analyze_feature_importance(results, X.columns)\n",
    "    \n",
    "    # 6. 客户风险分群\n",
    "    customer_risk_analysis(results, df, X_scaled, target_column, scaler)\n",
    "    \n",
    "    return df, results, X.columns, scaler\n",
    "\n",
    "def plot_results(results, y_test, df, target_column):\n",
    "    \"\"\"绘制结果可视化\"\"\"\n",
    "    valid_results = {k: v for k, v in results.items() if v is not None}\n",
    "    \n",
    "    if not valid_results:\n",
    "        print(\"没有有效的模型结果可可视化\")\n",
    "        return\n",
    "    \n",
    "    fig, axes = plt.subplots(2, 2, figsize=(15, 12))\n",
    "    \n",
    "    # 模型性能比较\n",
    "    model_names = list(valid_results.keys())\n",
    "    f1_scores = [valid_results[name]['metrics']['F1分数'] for name in model_names]\n",
    "    \n",
    "    axes[0,0].bar(model_names, f1_scores, color=['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FECA57'])\n",
    "    axes[0,0].set_title('模型F1分数比较', fontsize=14, fontweight='bold')\n",
    "    axes[0,0].set_ylabel('F1分数')\n",
    "    axes[0,0].tick_params(axis='x', rotation=45)\n",
    "    \n",
    "    # 客户流失分布\n",
    "    churn_counts = df[target_column].value_counts()\n",
    "    axes[0,1].pie(churn_counts.values, labels=['未流失', '流失'], autopct='%1.1f%%', \n",
    "                  colors=['#4ECDC4', '#FF6B6B'])\n",
    "    axes[0,1].set_title('客户流失分布', fontsize=14, fontweight='bold')\n",
    "    \n",
    "    # 混淆矩阵（最佳模型）\n",
    "    best_model_name = max(valid_results.items(), key=lambda x: x[1]['metrics']['F1分数'])[0]\n",
    "    best_predictions = valid_results[best_model_name]['predictions']\n",
    "    \n",
    "    cm = confusion_matrix(y_test, best_predictions)\n",
    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=axes[1,0],\n",
    "                xticklabels=['未流失', '流失'], yticklabels=['未流失', '流失'])\n",
    "    axes[1,0].set_title(f'最佳模型({best_model_name})混淆矩阵', fontsize=14, fontweight='bold')\n",
    "    axes[1,0].set_xlabel('预测标签')\n",
    "    axes[1,0].set_ylabel('真实标签')\n",
    "    \n",
    "    # 特征相关性热图\n",
    "    numeric_df = df.select_dtypes(include=[np.number])\n",
    "    if len(numeric_df.columns) > 1:\n",
    "        corr_matrix = numeric_df.corr()\n",
    "        sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', center=0, ax=axes[1,1], fmt='.2f')\n",
    "        axes[1,1].set_title('特征相关性热图', fontsize=14, fontweight='bold')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "def analyze_feature_importance(results, feature_names):\n",
    "    \"\"\"分析特征重要性\"\"\"\n",
    "    print(f\"\\n{'='*50}\")\n",
    "    print(\"特征重要性分析\")\n",
    "    print(f\"{'='*50}\")\n",
    "    \n",
    "    # 随机森林的特征重要性\n",
    "    if '随机森林' in results and results['随机森林'] is not None:\n",
    "        rf_model = results['随机森林']['model']\n",
    "        importance = rf_model.feature_importances_\n",
    "        \n",
    "        feature_imp_df = pd.DataFrame({\n",
    "            '特征': feature_names,\n",
    "            '重要性': importance\n",
    "        }).sort_values('重要性', ascending=False)\n",
    "        \n",
    "        print(\"\\n随机森林特征重要性排序:\")\n",
    "        print(feature_imp_df.to_string(index=False))\n",
    "        \n",
    "        plt.figure(figsize=(10, 6))\n",
    "        sns.barplot(data=feature_imp_df.head(10), x='重要性', y='特征', palette='viridis')\n",
    "        plt.title('特征重要性分析(随机森林)', fontsize=14, fontweight='bold')\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "def customer_risk_analysis(results, df, X_scaled, target_column, scaler):\n",
    "    \"\"\"客户风险分群分析\"\"\"\n",
    "    print(f\"\\n{'='*50}\")\n",
    "    print(\"客户风险分群分析\")\n",
    "    print(f\"{'='*50}\")\n",
    "    \n",
    "    valid_results = {k: v for k, v in results.items() if v is not None}\n",
    "    if not valid_results:\n",
    "        print(\"没有有效的模型进行风险分群分析\")\n",
    "        return\n",
    "    \n",
    "    best_model_name = max(valid_results.items(), key=lambda x: x[1]['metrics']['F1分数'])[0]\n",
    "    best_model = valid_results[best_model_name]['model']\n",
    "    \n",
    "    # 预测所有客户的流失概率\n",
    "    if hasattr(best_model, 'predict_proba'):\n",
    "        churn_probabilities = best_model.predict_proba(X_scaled)[:, 1]\n",
    "        df['流失概率'] = churn_probabilities\n",
    "        \n",
    "        # 风险等级划分\n",
    "        df['风险等级'] = pd.cut(df['流失概率'], \n",
    "                              bins=[0, 0.3, 0.7, 1], \n",
    "                              labels=['低风险', '中风险', '高风险'])\n",
    "        \n",
    "        # 风险分群统计\n",
    "        risk_stats = df.groupby('风险等级').agg({\n",
    "            '流失概率': ['mean', 'count'],\n",
    "            target_column: 'mean'\n",
    "        }).round(3)\n",
    "        \n",
    "        print(\"客户风险分群统计:\")\n",
    "        print(risk_stats)\n",
    "        \n",
    "        # 高风险客户特征分析\n",
    "        high_risk_customers = df[df['风险等级'] == '高风险']\n",
    "        print(f\"\\n高风险客户数量: {len(high_risk_customers)}\")\n",
    "        print(f\"高风险客户中实际流失比例: {high_risk_customers[target_column].mean():.2%}\")\n",
    "        \n",
    "        # 可视化风险分布\n",
    "        plt.figure(figsize=(12, 5))\n",
    "        \n",
    "        plt.subplot(1, 2, 1)\n",
    "        risk_counts = df['风险等级'].value_counts()\n",
    "        plt.pie(risk_counts.values, labels=risk_counts.index, autopct='%1.1f%%', \n",
    "                colors=['#4ECDC4', '#FECA57', '#FF6B6B'])\n",
    "        plt.title('客户风险等级分布')\n",
    "        \n",
    "        plt.subplot(1, 2, 2)\n",
    "        sns.boxplot(data=df, x='风险等级', y='流失概率')\n",
    "        plt.title('各风险等级流失概率分布')\n",
    "        \n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "        \n",
    "        # 业务建议\n",
    "        print(f\"\\n业务建议:\")\n",
    "        print(f\"1. 最佳预警模型: {best_model_name}\")\n",
    "        print(f\"2. 高风险客户占比: {len(high_risk_customers)/len(df)*100:.1f}%\")\n",
    "        print(f\"3. 建议重点关注: 高风险客户群体\")\n",
    "\n",
    "def predict_customer_churn(model, customer_data, feature_names, scaler):\n",
    "    \"\"\"预测单个客户流失概率\"\"\"\n",
    "    # 确保特征顺序一致\n",
    "    if len(customer_data) != len(feature_names):\n",
    "        customer_data = customer_data[:len(feature_names)]\n",
    "    \n",
    "    # 标准化特征\n",
    "    customer_scaled = scaler.transform([customer_data])\n",
    "    \n",
    "    if hasattr(model, 'predict_proba'):\n",
    "        churn_probability = model.predict_proba(customer_scaled)[0, 1]\n",
    "        return churn_probability\n",
    "    else:\n",
    "        prediction = model.predict(customer_scaled)[0]\n",
    "        return prediction\n",
    "\n",
    "# 执行分析\n",
    "if __name__ == \"__main__\":\n",
    "    df, results, feature_names, scaler = analyze_customer_churn()\n",
    "    \n",
    "    # 保存结果\n",
    "    try:\n",
    "        with pd.ExcelWriter('股票客户流失预警分析结果.xlsx') as writer:\n",
    "            df.to_excel(writer, sheet_name='原始数据', index=False)\n",
    "            \n",
    "            performance_data = []\n",
    "            for model_name, result in results.items():\n",
    "                if result is not None:\n",
    "                    performance_data.append({\n",
    "                        '模型': model_name,\n",
    "                        '准确率': result['metrics']['准确率'],\n",
    "                        '精确率': result['metrics']['精确率'],\n",
    "                        '召回率': result['metrics']['召回率'],\n",
    "                        'F1分数': result['metrics']['F1分数'],\n",
    "                        'AUC': result['metrics']['AUC'],\n",
    "                        '交叉验证F1': result['cv_mean']\n",
    "                    })\n",
    "            \n",
    "            pd.DataFrame(performance_data).to_excel(writer, sheet_name='模型性能', index=False)\n",
    "        \n",
    "        print(\"\\n分析结果已保存到 '股票客户流失预警分析结果.xlsx'\")\n",
    "    except Exception as e:\n",
    "        print(f\"保存文件时出错: {e}\")\n",
    "    \n",
    "    # 示例预测\n",
    "    valid_results = {k: v for k, v in results.items() if v is not None}\n",
    "    if valid_results:\n",
    "        best_model_name = max(valid_results.items(), key=lambda x: x[1]['metrics']['F1分数'])[0]\n",
    "        best_model = valid_results[best_model_name]['model']\n",
    "        \n",
    "        print(f\"\\n实际特征名称: {feature_names}\")\n",
    "        print(f\"特征数量: {len(feature_names)}\")\n",
    "        \n",
    "        # 创建示例客户数据\n",
    "        example_customer = [45, 50.0, 8, 5.0, 10.0, 0.03, 3, 15, 7, 1, 0][:len(feature_names)]\n",
    "        \n",
    "        churn_prob = predict_customer_churn(best_model, example_customer, feature_names, scaler)\n",
    "        risk_level = \"高风险\" if churn_prob > 0.7 else \"中风险\" if churn_prob > 0.3 else \"低风险\"\n",
    "        \n",
    "        print(f\"\\n示例客户流失预测:\")\n",
    "        print(f\"流失概率: {churn_prob:.4f}\")\n",
    "        print(f\"风险等级: {risk_level}\")\n",
    "        print(f\"建议措施: {'立即干预' if churn_prob > 0.7 else '重点关注' if churn_prob > 0.3 else '正常维护'}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e79f7575-3a9d-40b9-b7c8-cea067e449ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "对模型进行评估："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "350ad922-51f4-4574-b95e-df0e16e7f746",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix, roc_curve\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# 设置中文字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "def load_stock_data():\n",
    "    \"\"\"加载股票客户数据\"\"\"\n",
    "    try:\n",
    "        file_path = r\"d:\\桌面\\商业数据分析\\@Python大数据分析与机器学习商业案例实战\\第4章 逻辑回归模型\\源代码汇总_Jupyter Notebook格式（推荐）\\股票客户流失.xlsx\"\n",
    "        df = pd.read_excel(file_path)\n",
    "        print(\"成功读取股票客户流失数据\")\n",
    "        print(f\"数据形状: {df.shape}\")\n",
    "        return df\n",
    "    except Exception as e:\n",
    "        print(f\"读取文件失败: {e}\")\n",
    "        print(\"使用模拟数据进行分析...\")\n",
    "        return generate_sample_stock_data()\n",
    "\n",
    "def generate_sample_stock_data():\n",
    "    \"\"\"生成模拟的股票客户流失数据\"\"\"\n",
    "    np.random.seed(42)\n",
    "    n_samples = 2000\n",
    "    \n",
    "    data = {\n",
    "        '年龄': np.random.randint(25, 70, n_samples),\n",
    "        '资产规模_万元': np.random.uniform(1, 500, n_samples),\n",
    "        '月交易频率': np.random.randint(1, 50, n_samples),\n",
    "        '平均交易金额_万元': np.random.uniform(0.1, 50, n_samples),\n",
    "        '账户余额_万元': np.random.uniform(0.1, 100, n_samples),\n",
    "        '佣金费率': np.random.uniform(0.01, 0.05, n_samples),\n",
    "        '持有产品数量': np.random.randint(1, 10, n_samples),\n",
    "        '最近登录天数': np.random.randint(1, 90, n_samples),\n",
    "        '客户服务评分': np.random.randint(1, 10, n_samples),\n",
    "        '是否使用移动端': np.random.choice([0, 1], n_samples, p=[0.3, 0.7]),\n",
    "    }\n",
    "    \n",
    "    df = pd.DataFrame(data)\n",
    "    \n",
    "    # 生成流失标签（基于多个特征的逻辑组合）\n",
    "    churn_prob = (\n",
    "        0.1 +\n",
    "        (df['年龄'] > 60) * 0.2 +\n",
    "        (df['资产规模_万元'] < 10) * 0.3 +\n",
    "        (df['月交易频率'] < 5) * 0.4 +\n",
    "        (df['最近登录天数'] > 30) * 0.5 +\n",
    "        (df['客户服务评分'] < 3) * 0.3 +\n",
    "        (df['是否使用移动端'] == 0) * 0.2 +\n",
    "        np.random.normal(0, 0.3, n_samples)\n",
    "    )\n",
    "    \n",
    "    df['是否流失'] = (churn_prob > 0.5).astype(int)\n",
    "    \n",
    "    print(f\"流失客户比例: {df['是否流失'].mean():.2%}\")\n",
    "    return df\n",
    "\n",
    "def get_classification_models():\n",
    "    \"\"\"定义五种分类模型\"\"\"\n",
    "    models = {\n",
    "        '逻辑回归': LogisticRegression(random_state=42, max_iter=1000),\n",
    "        '随机森林': RandomForestClassifier(n_estimators=100, random_state=42),\n",
    "        '梯度提升': GradientBoostingClassifier(n_estimators=100, random_state=42),\n",
    "        '支持向量机': SVC(probability=True, random_state=42),\n",
    "        'K近邻': KNeighborsClassifier(n_neighbors=5)\n",
    "    }\n",
    "    return models\n",
    "\n",
    "def evaluate_auc_performance(auc_score):\n",
    "    \"\"\"评估AUC性能\"\"\"\n",
    "    if auc_score >= 0.85:\n",
    "        return \"非常不错\", \"🟢\"\n",
    "    elif auc_score >= 0.75:\n",
    "        return \"可以接受\", \"🟡\"\n",
    "    else:\n",
    "        return \"需要改进\", \"🔴\"\n",
    "\n",
    "def analyze_customer_churn():\n",
    "    \"\"\"主分析函数\"\"\"\n",
    "    print(\"=\" * 60)\n",
    "    print(\"股票客户流失预警模型评估(AUC指标)\")\n",
    "    print(\"=\" * 60)\n",
    "    \n",
    "    # 1. 加载数据\n",
    "    df = load_stock_data()\n",
    "    \n",
    "    # 2. 数据预处理\n",
    "    target_column = '是否流失' if '是否流失' in df.columns else df.columns[-1]\n",
    "    X = df.drop(target_column, axis=1)\n",
    "    y = df[target_column]\n",
    "    \n",
    "    print(f\"流失率: {y.mean():.2%}\")\n",
    "    \n",
    "    # 处理非数值型数据\n",
    "    for col in X.select_dtypes(include=['object']).columns:\n",
    "        if X[col].nunique() <= 10:\n",
    "            X[col] = X[col].astype('category').cat.codes\n",
    "    \n",
    "    # 特征标准化\n",
    "    scaler = StandardScaler()\n",
    "    X_scaled = scaler.fit_transform(X)\n",
    "    \n",
    "    # 划分训练集和测试集\n",
    "    X_train, X_test, y_train, y_test = train_test_split(\n",
    "        X_scaled, y, test_size=0.2, random_state=42, stratify=y\n",
    "    )\n",
    "    \n",
    "    # 3. 模型训练和评估\n",
    "    models = get_classification_models()\n",
    "    results = {}\n",
    "    \n",
    "    print(f\"\\n{'='*50}\")\n",
    "    print(\"模型AUC性能评估\")\n",
    "    print(f\"{'='*50}\")\n",
    "    print(\"AUC评估标准:\")\n",
    "    print(\"🟢 AUC ≥ 0.85: 非常不错的模型\")\n",
    "    print(\"🟡 0.75 ≤ AUC < 0.85: 可以接受的模型\") \n",
    "    print(\"🔴 AUC < 0.75: 需要改进的模型\")\n",
    "    print(f\"{'='*50}\")\n",
    "    \n",
    "    for name, model in models.items():\n",
    "        print(f\"\\n训练{name}...\")\n",
    "        \n",
    "        try:\n",
    "            model.fit(X_train, y_train)\n",
    "            y_pred_proba = model.predict_proba(X_test)[:, 1]\n",
    "            auc_score = roc_auc_score(y_test, y_pred_proba)\n",
    "            \n",
    "            # 交叉验证AUC\n",
    "            cv_auc_scores = cross_val_score(model, X_scaled, y, cv=5, scoring='roc_auc')\n",
    "            \n",
    "            performance, emoji = evaluate_auc_performance(auc_score)\n",
    "            \n",
    "            results[name] = {\n",
    "                'model': model,\n",
    "                'auc': auc_score,\n",
    "                'cv_auc_mean': cv_auc_scores.mean(),\n",
    "                'cv_auc_std': cv_auc_scores.std(),\n",
    "                'y_pred_proba': y_pred_proba,\n",
    "                'performance': performance,\n",
    "                'emoji': emoji\n",
    "            }\n",
    "            \n",
    "            print(f\"{name:.<12} {emoji} AUC: {auc_score:.4f} - {performance}\")\n",
    "            print(f\"{' ':12} 交叉验证AUC: {cv_auc_scores.mean():.4f} (±{cv_auc_scores.std():.4f})\")\n",
    "            \n",
    "        except Exception as e:\n",
    "            print(f\"{name} 训练失败: {e}\")\n",
    "            results[name] = None\n",
    "    \n",
    "    # 4. 可视化AUC比较\n",
    "    plot_auc_comparison(results, y_test)\n",
    "    \n",
    "    # 5. 最佳模型分析\n",
    "    best_model_analysis(results, y_test)\n",
    "    \n",
    "    return df, results, X.columns, scaler\n",
    "\n",
    "def plot_auc_comparison(results, y_test):\n",
    "    \"\"\"绘制AUC比较图\"\"\"\n",
    "    valid_results = {k: v for k, v in results.items() if v is not None}\n",
    "    \n",
    "    if not valid_results:\n",
    "        print(\"没有有效的模型结果可可视化\")\n",
    "        return\n",
    "    \n",
    "    fig, axes = plt.subplots(1, 2, figsize=(15, 6))\n",
    "    \n",
    "    # 1. AUC值比较柱状图\n",
    "    model_names = list(valid_results.keys())\n",
    "    auc_scores = [valid_results[name]['auc'] for name in model_names]\n",
    "    colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FECA57']\n",
    "    \n",
    "    bars = axes[0].bar(model_names, auc_scores, color=colors)\n",
    "    axes[0].set_title('模型AUC值比较', fontsize=14, fontweight='bold')\n",
    "    axes[0].set_ylabel('AUC值')\n",
    "    axes[0].tick_params(axis='x', rotation=45)\n",
    "    \n",
    "    # 添加性能标注\n",
    "    for bar, auc_score in zip(bars, auc_scores):\n",
    "        performance, emoji = evaluate_auc_performance(auc_score)\n",
    "        height = bar.get_height()\n",
    "        axes[0].text(bar.get_x() + bar.get_width()/2., height + 0.01,\n",
    "                    f'{emoji}\\n{auc_score:.3f}', ha='center', va='bottom')\n",
    "    \n",
    "    # 添加参考线\n",
    "    axes[0].axhline(y=0.75, color='red', linestyle='--', alpha=0.7, label='可接受阈值(0.75)')\n",
    "    axes[0].axhline(y=0.85, color='green', linestyle='--', alpha=0.7, label='优秀阈值(0.85)')\n",
    "    axes[0].legend()\n",
    "    \n",
    "    # 2. ROC曲线\n",
    "    axes[1].plot([0, 1], [0, 1], 'k--', label='随机分类器 (AUC = 0.5)')\n",
    "    \n",
    "    for name, result in valid_results.items():\n",
    "        fpr, tpr, _ = roc_curve(y_test, result['y_pred_proba'])\n",
    "        auc_score = result['auc']\n",
    "        axes[1].plot(fpr, tpr, label=f'{name} (AUC = {auc_score:.3f})', linewidth=2)\n",
    "    \n",
    "    axes[1].set_xlabel('假正率 (False Positive Rate)')\n",
    "    axes[1].set_ylabel('真正率 (True Positive Rate)')\n",
    "    axes[1].set_title('ROC曲线比较', fontsize=14, fontweight='bold')\n",
    "    axes[1].legend()\n",
    "    axes[1].grid(True, alpha=0.3)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "def best_model_analysis(results, y_test):\n",
    "    \"\"\"最佳模型详细分析\"\"\"\n",
    "    valid_results = {k: v for k, v in results.items() if v is not None}\n",
    "    \n",
    "    if not valid_results:\n",
    "        return\n",
    "    \n",
    "    # 找到AUC最高的模型\n",
    "    best_model_name = max(valid_results.items(), key=lambda x: x[1]['auc'])[0]\n",
    "    best_result = valid_results[best_model_name]\n",
    "    \n",
    "    print(f\"\\n{'='*50}\")\n",
    "    print(f\"最佳模型分析: {best_model_name}\")\n",
    "    print(f\"{'='*50}\")\n",
    "    \n",
    "    auc_score = best_result['auc']\n",
    "    performance, emoji = evaluate_auc_performance(auc_score)\n",
    "    \n",
    "    print(f\"{emoji} AUC值: {auc_score:.4f} - {performance}\")\n",
    "    print(f\"交叉验证AUC: {best_result['cv_auc_mean']:.4f} (±{best_result['cv_auc_std']:.4f})\")\n",
    "    \n",
    "    # 业务解释\n",
    "    print(f\"\\n业务解释:\")\n",
    "    if auc_score >= 0.85:\n",
    "        print(\"• 模型具有优秀的区分能力，能准确识别流失风险\")\n",
    "        print(\"• 可以信赖模型的预测结果进行客户干预\")\n",
    "        print(\"• 建议在生产环境中部署使用\")\n",
    "    elif auc_score >= 0.75:\n",
    "        print(\"• 模型具有较好的预测能力，可以作为决策参考\")\n",
    "        print(\"• 建议结合业务经验进行客户干预\")\n",
    "        print(\"• 可以考虑进一步优化模型\")\n",
    "    else:\n",
    "        print(\"• 模型预测能力有待提升\")\n",
    "        print(\"• 建议重新进行特征工程或尝试其他算法\")\n",
    "        print(\"• 当前模型仅作为参考使用\")\n",
    "    \n",
    "    # 计算不同阈值下的业务指标\n",
    "    y_pred_proba = best_result['y_pred_proba']\n",
    "    \n",
    "    print(f\"\\n不同阈值下的业务指标:\")\n",
    "    print(\"-\" * 40)\n",
    "    print(\"阈值 | 精确率 | 召回率 | 干预客户比例\")\n",
    "    print(\"-\" * 40)\n",
    "    \n",
    "    for threshold in [0.3, 0.5, 0.7]:\n",
    "        y_pred = (y_pred_proba >= threshold).astype(int)\n",
    "        precision = precision_score(y_test, y_pred, zero_division=0)\n",
    "        recall = recall_score(y_test, y_pred, zero_division=0)\n",
    "        intervention_ratio = (y_pred == 1).mean()\n",
    "        \n",
    "        print(f\"{threshold:.1f}   | {precision:.3f}  | {recall:.3f}  | {intervention_ratio:.1%}\")\n",
    "\n",
    "def predict_customer_risk(model, customer_data, feature_names, scaler):\n",
    "    \"\"\"预测客户流失风险\"\"\"\n",
    "    if len(customer_data) != len(feature_names):\n",
    "        customer_data = customer_data[:len(feature_names)]\n",
    "    \n",
    "    customer_scaled = scaler.transform([customer_data])\n",
    "    churn_prob = model.predict_proba(customer_scaled)[0, 1]\n",
    "    \n",
    "    return churn_prob\n",
    "\n",
    "# 执行分析\n",
    "if __name__ == \"__main__\":\n",
    "    df, results, feature_names, scaler = analyze_customer_churn()\n",
    "    \n",
    "    # 保存AUC评估结果\n",
    "    try:\n",
    "        auc_summary = []\n",
    "        for model_name, result in results.items():\n",
    "            if result is not None:\n",
    "                auc_summary.append({\n",
    "                    '模型': model_name,\n",
    "                    'AUC值': result['auc'],\n",
    "                    '性能评价': result['performance'],\n",
    "                    '交叉验证AUC': result['cv_auc_mean'],\n",
    "                    '交叉验证标准差': result['cv_auc_std']\n",
    "                })\n",
    "        \n",
    "        auc_df = pd.DataFrame(auc_summary).sort_values('AUC值', ascending=False)\n",
    "        \n",
    "        with pd.ExcelWriter('股票客户流失AUC评估结果.xlsx') as writer:\n",
    "            auc_df.to_excel(writer, sheet_name='AUC评估', index=False)\n",
    "            df.to_excel(writer, sheet_name='原始数据', index=False)\n",
    "        \n",
    "        print(f\"\\nAUC评估结果已保存到 '股票客户流失AUC评估结果.xlsx'\")\n",
    "        \n",
    "        # 显示AUC排名\n",
    "        print(f\"\\n模型AUC排名:\")\n",
    "        print(\"-\" * 50)\n",
    "        for i, row in auc_df.iterrows():\n",
    "            print(f\"{i+1}. {row['模型']:.<12} AUC: {row['AUC值']:.4f} - {row['性能评价']}\")\n",
    "            \n",
    "    except Exception as e:\n",
    "        print(f\"保存文件时出错: {e}\")\n",
    "    \n",
    "    # 示例预测\n",
    "    valid_results = {k: v for k, v in results.items() if v is not None}\n",
    "    if valid_results:\n",
    "        best_model_name = max(valid_results.items(), key=lambda x: x[1]['auc'])[0]\n",
    "        best_model = valid_results[best_model_name]['model']\n",
    "        \n",
    "        print(f\"\\n使用最佳模型进行预测: {best_model_name}\")\n",
    "        \n",
    "        # 创建示例客户数据\n",
    "        example_customer = [45, 50.0, 8, 5.0, 10.0, 0.03, 3, 15, 7, 1][:len(feature_names)]\n",
    "        \n",
    "        churn_prob = predict_customer_risk(best_model, example_customer, feature_names, scaler)\n",
    "        risk_level = \"高风险\" if churn_prob > 0.7 else \"中风险\" if churn_prob > 0.3 else \"低风险\"\n",
    "        \n",
    "        print(f\"\\n示例客户流失风险评估:\")\n",
    "        print(f\"流失概率: {churn_prob:.4f}\")\n",
    "        print(f\"风险等级: {risk_level}\")\n",
    "        print(f\"建议措施: {'立即干预' if churn_prob > 0.7 else '重点关注' if churn_prob > 0.3 else '正常维护'}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6fed718-d96e-47e1-8d31-5743402a0340",
   "metadata": {},
   "outputs": [],
   "source": [
    "案例4：员工离职预测模型搭建"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c177f682-8fca-45a0-b831-035cc4b90f9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix, classification_report, roc_curve\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.tree import export_graphviz\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# 设置中文字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "def load_employee_data():\n",
    "    \"\"\"加载员工离职数据\"\"\"\n",
    "    try:\n",
    "        file_path = r\"d:\\桌面\\商业数据分析\\@Python大数据分析与机器学习商业案例实战\\第5章 决策树模型\\源代码汇总_Jupyter Notebook格式（推荐）\\员工离职预测模型.xlsx\"\n",
    "        df = pd.read_excel(file_path)\n",
    "        print(\"成功读取员工离职预测数据\")\n",
    "        print(f\"数据形状: {df.shape}\")\n",
    "        print(f\"实际列名: {df.columns.tolist()}\")\n",
    "        return df\n",
    "    except Exception as e:\n",
    "        print(f\"读取文件失败: {e}\")\n",
    "        print(\"使用模拟数据进行分析...\")\n",
    "        return generate_sample_employee_data()\n",
    "\n",
    "def generate_sample_employee_data():\n",
    "    \"\"\"生成模拟的员工离职数据\"\"\"\n",
    "    np.random.seed(42)\n",
    "    n_samples = 1500\n",
    "    \n",
    "    data = {\n",
    "        '年龄': np.random.randint(22, 60, n_samples),\n",
    "        '月收入': np.random.randint(3000, 20000, n_samples),\n",
    "        '工作年限': np.random.randint(1, 15, n_samples),\n",
    "        '最近绩效评分': np.random.randint(1, 6, n_samples),\n",
    "        '加班时长_月': np.random.randint(0, 60, n_samples),\n",
    "        '项目完成数': np.random.randint(0, 20, n_samples),\n",
    "        '工作满意度': np.random.randint(1, 6, n_samples),\n",
    "        '晋升次数': np.random.randint(0, 4, n_samples),\n",
    "        '培训次数': np.random.randint(0, 10, n_samples),\n",
    "        '部门': np.random.choice(['技术部', '销售部', '市场部', '人事部', '财务部'], n_samples),\n",
    "        '职位级别': np.random.choice(['初级', '中级', '高级', '管理'], n_samples),\n",
    "    }\n",
    "    \n",
    "    df = pd.DataFrame(data)\n",
    "    \n",
    "    # 生成离职标签（基于多个特征的逻辑组合）\n",
    "    turnover_prob = (\n",
    "        0.1 +\n",
    "        (df['工作满意度'] < 3) * 0.3 +\n",
    "        (df['最近绩效评分'] < 3) * 0.2 +\n",
    "        (df['月收入'] < 5000) * 0.2 +\n",
    "        (df['加班时长_月'] > 40) * 0.15 +\n",
    "        (df['晋升次数'] == 0) * 0.1 +\n",
    "        (df['工作年限'] > 10) * 0.05 +\n",
    "        np.random.normal(0, 0.2, n_samples)\n",
    "    )\n",
    "    \n",
    "    df['是否离职'] = (turnover_prob > 0.5).astype(int)\n",
    "    \n",
    "    print(f\"离职员工比例: {df['是否离职'].mean():.2%}\")\n",
    "    return df\n",
    "\n",
    "def get_classification_models():\n",
    "    \"\"\"定义五种分类模型\"\"\"\n",
    "    models = {\n",
    "        '决策树': DecisionTreeClassifier(random_state=42, max_depth=5),\n",
    "        '随机森林': RandomForestClassifier(n_estimators=100, random_state=42),\n",
    "        '梯度提升': GradientBoostingClassifier(n_estimators=100, random_state=42),\n",
    "        '逻辑回归': LogisticRegression(random_state=42, max_iter=1000),\n",
    "        '支持向量机': SVC(probability=True, random_state=42)\n",
    "    }\n",
    "    return models\n",
    "\n",
    "def evaluate_auc_performance(auc_score):\n",
    "    \"\"\"评估AUC性能\"\"\"\n",
    "    if auc_score >= 0.85:\n",
    "        return \"非常不错\", \"🟢\"\n",
    "    elif auc_score >= 0.75:\n",
    "        return \"可以接受\", \"🟡\"\n",
    "    else:\n",
    "        return \"需要改进\", \"🔴\"\n",
    "\n",
    "def analyze_employee_turnover():\n",
    "    \"\"\"主分析函数\"\"\"\n",
    "    print(\"=\" * 60)\n",
    "    print(\"员工离职预测模型分析\")\n",
    "    print(\"=\" * 60)\n",
    "    \n",
    "    # 1. 加载数据\n",
    "    df = load_employee_data()\n",
    "    print(f\"\\n数据预览:\")\n",
    "    print(df.head())\n",
    "    \n",
    "    # 2. 数据探索\n",
    "    print(f\"\\n数据探索:\")\n",
    "    print(f\"总员工数: {len(df)}\")\n",
    "    if '是否离职' in df.columns:\n",
    "        turnover_rate = df['是否离职'].mean()\n",
    "        print(f\"离职员工数: {df['是否离职'].sum()}\")\n",
    "        print(f\"离职率: {turnover_rate:.2%}\")\n",
    "    \n",
    "    # 3. 数据预处理\n",
    "    # 自动识别目标变量\n",
    "    target_keywords = ['离职', 'turnover', '是否离职', '是否辞职', 'Attrition']\n",
    "    target_column = None\n",
    "    \n",
    "    for col in df.columns:\n",
    "        for keyword in target_keywords:\n",
    "            if keyword.lower() in str(col).lower():\n",
    "                target_column = col\n",
    "                break\n",
    "        if target_column:\n",
    "            break\n",
    "    \n",
    "    if target_column is None:\n",
    "        target_column = df.columns[-1]\n",
    "        print(f\"未找到明确的目标变量列，使用最后一列: {target_column}\")\n",
    "    \n",
    "    print(f\"使用目标变量: {target_column}\")\n",
    "    \n",
    "    X = df.drop(target_column, axis=1)\n",
    "    y = df[target_column]\n",
    "    \n",
    "    # 处理分类变量\n",
    "    categorical_columns = X.select_dtypes(include=['object']).columns\n",
    "    print(f\"\\n分类变量: {categorical_columns.tolist()}\")\n",
    "    \n",
    "    label_encoders = {}\n",
    "    for col in categorical_columns:\n",
    "        le = LabelEncoder()\n",
    "        X[col] = le.fit_transform(X[col])\n",
    "        label_encoders[col] = le\n",
    "        print(f\"编码变量: {col} -> {list(le.classes_)}\")\n",
    "    \n",
    "    # 特征标准化\n",
    "    scaler = StandardScaler()\n",
    "    X_scaled = scaler.fit_transform(X)\n",
    "    \n",
    "    # 划分训练集和测试集\n",
    "    X_train, X_test, y_train, y_test = train_test_split(\n",
    "        X_scaled, y, test_size=0.2, random_state=42, stratify=y\n",
    "    )\n",
    "    \n",
    "    # 4. 模型训练和评估\n",
    "    models = get_classification_models()\n",
    "    results = {}\n",
    "    \n",
    "    print(f\"\\n{'='*50}\")\n",
    "    print(\"模型AUC性能评估\")\n",
    "    print(f\"{'='*50}\")\n",
    "    print(\"AUC评估标准:\")\n",
    "    print(\"🟢 AUC ≥ 0.85: 非常不错的模型\")\n",
    "    print(\"🟡 0.75 ≤ AUC < 0.85: 可以接受的模型\") \n",
    "    print(\"🔴 AUC < 0.75: 需要改进的模型\")\n",
    "    print(f\"{'='*50}\")\n",
    "    \n",
    "    for name, model in models.items():\n",
    "        print(f\"\\n训练{name}...\")\n",
    "        \n",
    "        try:\n",
    "            model.fit(X_train, y_train)\n",
    "            y_pred_proba = model.predict_proba(X_test)[:, 1]\n",
    "            auc_score = roc_auc_score(y_test, y_pred_proba)\n",
    "            \n",
    "            # 交叉验证AUC\n",
    "            cv_auc_scores = cross_val_score(model, X_scaled, y, cv=5, scoring='roc_auc')\n",
    "            \n",
    "            performance, emoji = evaluate_auc_performance(auc_score)\n",
    "            \n",
    "            results[name] = {\n",
    "                'model': model,\n",
    "                'auc': auc_score,\n",
    "                'cv_auc_mean': cv_auc_scores.mean(),\n",
    "                'cv_auc_std': cv_auc_scores.std(),\n",
    "                'y_pred_proba': y_pred_proba,\n",
    "                'performance': performance,\n",
    "                'emoji': emoji,\n",
    "                'feature_names': X.columns.tolist()\n",
    "            }\n",
    "            \n",
    "            print(f\"{name:.<12} {emoji} AUC: {auc_score:.4f} - {performance}\")\n",
    "            print(f\"{' ':12} 交叉验证AUC: {cv_auc_scores.mean():.4f} (±{cv_auc_scores.std():.4f})\")\n",
    "            \n",
    "        except Exception as e:\n",
    "            print(f\"{name} 训练失败: {e}\")\n",
    "            results[name] = None\n",
    "    \n",
    "    # 5. 可视化分析\n",
    "    plot_model_comparison(results, y_test, df, target_column)\n",
    "    \n",
    "    # 6. 特征重要性分析\n",
    "    analyze_feature_importance(results, X.columns)\n",
    "    \n",
    "    # 7. 员工风险分群\n",
    "    employee_risk_analysis(results, df, X_scaled, target_column, scaler)\n",
    "    \n",
    "    return df, results, X.columns, scaler, label_encoders\n",
    "\n",
    "def plot_model_comparison(results, y_test, df, target_column):\n",
    "    \"\"\"绘制模型比较图\"\"\"\n",
    "    valid_results = {k: v for k, v in results.items() if v is not None}\n",
    "    \n",
    "    if not valid_results:\n",
    "        print(\"没有有效的模型结果可可视化\")\n",
    "        return\n",
    "    \n",
    "    fig, axes = plt.subplots(2, 2, figsize=(15, 12))\n",
    "    \n",
    "    # 1. AUC值比较柱状图\n",
    "    model_names = list(valid_results.keys())\n",
    "    auc_scores = [valid_results[name]['auc'] for name in model_names]\n",
    "    colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FECA57']\n",
    "    \n",
    "    bars = axes[0,0].bar(model_names, auc_scores, color=colors)\n",
    "    axes[0,0].set_title('模型AUC值比较', fontsize=14, fontweight='bold')\n",
    "    axes[0,0].set_ylabel('AUC值')\n",
    "    axes[0,0].tick_params(axis='x', rotation=45)\n",
    "    \n",
    "    # 添加性能标注\n",
    "    for bar, auc_score in zip(bars, auc_scores):\n",
    "        performance, emoji = evaluate_auc_performance(auc_score)\n",
    "        height = bar.get_height()\n",
    "        axes[0,0].text(bar.get_x() + bar.get_width()/2., height + 0.01,\n",
    "                    f'{emoji}\\n{auc_score:.3f}', ha='center', va='bottom')\n",
    "    \n",
    "    # 添加参考线\n",
    "    axes[0,0].axhline(y=0.75, color='red', linestyle='--', alpha=0.7, label='可接受阈值(0.75)')\n",
    "    axes[0,0].axhline(y=0.85, color='green', linestyle='--', alpha=0.7, label='优秀阈值(0.85)')\n",
    "    axes[0,0].legend()\n",
    "    \n",
    "    # 2. 员工离职分布\n",
    "    turnover_counts = df[target_column].value_counts()\n",
    "    axes[0,1].pie(turnover_counts.values, labels=['在职', '离职'], autopct='%1.1f%%', \n",
    "                  colors=['#4ECDC4', '#FF6B6B'])\n",
    "    axes[0,1].set_title('员工离职分布', fontsize=14, fontweight='bold')\n",
    "    \n",
    "    # 3. ROC曲线\n",
    "    axes[1,0].plot([0, 1], [0, 1], 'k--', label='随机分类器 (AUC = 0.5)')\n",
    "    \n",
    "    for name, result in valid_results.items():\n",
    "        fpr, tpr, _ = roc_curve(y_test, result['y_pred_proba'])\n",
    "        auc_score = result['auc']\n",
    "        axes[1,0].plot(fpr, tpr, label=f'{name} (AUC = {auc_score:.3f})', linewidth=2)\n",
    "    \n",
    "    axes[1,0].set_xlabel('假正率 (False Positive Rate)')\n",
    "    axes[1,0].set_ylabel('真正率 (True Positive Rate)')\n",
    "    axes[1,0].set_title('ROC曲线比较', fontsize=14, fontweight='bold')\n",
    "    axes[1,0].legend()\n",
    "    axes[1,0].grid(True, alpha=0.3)\n",
    "    \n",
    "    # 4. 特征相关性热图\n",
    "    numeric_df = df.select_dtypes(include=[np.number])\n",
    "    if len(numeric_df.columns) > 1:\n",
    "        corr_matrix = numeric_df.corr()\n",
    "        sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', center=0, ax=axes[1,1], fmt='.2f')\n",
    "        axes[1,1].set_title('特征相关性热图', fontsize=14, fontweight='bold')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "def analyze_feature_importance(results, feature_names):\n",
    "    \"\"\"分析特征重要性\"\"\"\n",
    "    print(f\"\\n{'='*50}\")\n",
    "    print(\"特征重要性分析\")\n",
    "    print(f\"{'='*50}\")\n",
    "    \n",
    "    # 随机森林的特征重要性\n",
    "    if '随机森林' in results and results['随机森林'] is not None:\n",
    "        rf_model = results['随机森林']['model']\n",
    "        importance = rf_model.feature_importances_\n",
    "        \n",
    "        feature_imp_df = pd.DataFrame({\n",
    "            '特征': feature_names,\n",
    "            '重要性': importance\n",
    "        }).sort_values('重要性', ascending=False)\n",
    "        \n",
    "        print(\"\\n随机森林特征重要性排序:\")\n",
    "        print(feature_imp_df.to_string(index=False))\n",
    "        \n",
    "        # 可视化特征重要性\n",
    "        plt.figure(figsize=(12, 6))\n",
    "        sns.barplot(data=feature_imp_df.head(10), x='重要性', y='特征', palette='viridis')\n",
    "        plt.title('员工离职影响因素重要性分析', fontsize=14, fontweight='bold')\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "    \n",
    "    # 决策树特征重要性\n",
    "    if '决策树' in results and results['决策树'] is not None:\n",
    "        dt_model = results['决策树']['model']\n",
    "        dt_importance = dt_model.feature_importances_\n",
    "        \n",
    "        dt_imp_df = pd.DataFrame({\n",
    "            '特征': feature_names,\n",
    "            '重要性': dt_importance\n",
    "        }).sort_values('重要性', ascending=False)\n",
    "        \n",
    "        print(\"\\n决策树特征重要性排序:\")\n",
    "        print(dt_imp_df.head(8).to_string(index=False))\n",
    "\n",
    "def employee_risk_analysis(results, df, X_scaled, target_column, scaler):\n",
    "    \"\"\"员工风险分群分析\"\"\"\n",
    "    print(f\"\\n{'='*50}\")\n",
    "    print(\"员工离职风险分群分析\")\n",
    "    print(f\"{'='*50}\")\n",
    "    \n",
    "    valid_results = {k: v for k, v in results.items() if v is not None}\n",
    "    if not valid_results:\n",
    "        print(\"没有有效的模型进行风险分群分析\")\n",
    "        return\n",
    "    \n",
    "    best_model_name = max(valid_results.items(), key=lambda x: x[1]['auc'])[0]\n",
    "    best_model = valid_results[best_model_name]['model']\n",
    "    \n",
    "    # 预测所有员工的离职概率\n",
    "    if hasattr(best_model, 'predict_proba'):\n",
    "        turnover_probabilities = best_model.predict_proba(X_scaled)[:, 1]\n",
    "        df['离职概率'] = turnover_probabilities\n",
    "        \n",
    "        # 风险等级划分\n",
    "        df['风险等级'] = pd.cut(df['离职概率'], \n",
    "                              bins=[0, 0.3, 0.7, 1], \n",
    "                              labels=['低风险', '中风险', '高风险'])\n",
    "        \n",
    "        # 风险分群统计\n",
    "        risk_stats = df.groupby('风险等级').agg({\n",
    "            '离职概率': ['mean', 'count'],\n",
    "            target_column: 'mean'\n",
    "        }).round(3)\n",
    "        \n",
    "        print(\"员工风险分群统计:\")\n",
    "        print(risk_stats)\n",
    "        \n",
    "        # 高风险员工特征分析\n",
    "        high_risk_employees = df[df['风险等级'] == '高风险']\n",
    "        print(f\"\\n高风险员工数量: {len(high_risk_employees)}\")\n",
    "        print(f\"高风险员工中实际离职比例: {high_risk_employees[target_column].mean():.2%}\")\n",
    "        \n",
    "        # 可视化风险分布\n",
    "        plt.figure(figsize=(12, 5))\n",
    "        \n",
    "        plt.subplot(1, 2, 1)\n",
    "        risk_counts = df['风险等级'].value_counts()\n",
    "        plt.pie(risk_counts.values, labels=risk_counts.index, autopct='%1.1f%%', \n",
    "                colors=['#4ECDC4', '#FECA57', '#FF6B6B'])\n",
    "        plt.title('员工离职风险等级分布')\n",
    "        \n",
    "        plt.subplot(1, 2, 2)\n",
    "        sns.boxplot(data=df, x='风险等级', y='离职概率')\n",
    "        plt.title('各风险等级离职概率分布')\n",
    "        \n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "        \n",
    "        # 业务建议\n",
    "        print(f\"\\n人力资源管理建议:\")\n",
    "        print(f\"1. 最佳预测模型: {best_model_name}\")\n",
    "        print(f\"2. 高风险员工占比: {len(high_risk_employees)/len(df)*100:.1f}%\")\n",
    "        print(f\"3. 建议重点关注: 工作满意度低、绩效评分差、加班时间长的员工\")\n",
    "\n",
    "def predict_employee_turnover(model, employee_data, feature_names, scaler):\n",
    "    \"\"\"预测员工离职概率\"\"\"\n",
    "    if len(employee_data) != len(feature_names):\n",
    "        employee_data = employee_data[:len(feature_names)]\n",
    "    \n",
    "    employee_scaled = scaler.transform([employee_data])\n",
    "    turnover_prob = model.predict_proba(employee_scaled)[0, 1]\n",
    "    \n",
    "    return turnover_prob\n",
    "\n",
    "# 执行分析\n",
    "if __name__ == \"__main__\":\n",
    "    df, results, feature_names, scaler, label_encoders = analyze_employee_turnover()\n",
    "    \n",
    "    # 保存评估结果\n",
    "    try:\n",
    "        auc_summary = []\n",
    "        for model_name, result in results.items():\n",
    "            if result is not None:\n",
    "                auc_summary.append({\n",
    "                    '模型': model_name,\n",
    "                    'AUC值': result['auc'],\n",
    "                    '性能评价': result['performance'],\n",
    "                    '交叉验证AUC': result['cv_auc_mean'],\n",
    "                    '交叉验证标准差': result['cv_auc_std']\n",
    "                })\n",
    "        \n",
    "        auc_df = pd.DataFrame(auc_summary).sort_values('AUC值', ascending=False)\n",
    "        \n",
    "        with pd.ExcelWriter('员工离职预测模型评估结果.xlsx') as writer:\n",
    "            auc_df.to_excel(writer, sheet_name='AUC评估', index=False)\n",
    "            df.to_excel(writer, sheet_name='员工数据', index=False)\n",
    "            \n",
    "            # 保存特征重要性\n",
    "            if '随机森林' in results and results['随机森林'] is not None:\n",
    "                rf_model = results['随机森林']['model']\n",
    "                importance = rf_model.feature_importances_\n",
    "                feature_imp_df = pd.DataFrame({\n",
    "                    '特征': feature_names,\n",
    "                    '重要性': importance\n",
    "                }).sort_values('重要性', ascending=False)\n",
    "                feature_imp_df.to_excel(writer, sheet_name='特征重要性', index=False)\n",
    "        \n",
    "        print(f\"\\n评估结果已保存到 '员工离职预测模型评估结果.xlsx'\")\n",
    "        \n",
    "        # 显示AUC排名\n",
    "        print(f\"\\n模型AUC排名:\")\n",
    "        print(\"-\" * 50)\n",
    "        for i, row in auc_df.iterrows():\n",
    "            print(f\"{i+1}. {row['模型']:.<12} AUC: {row['AUC值']:.4f} - {row['性能评价']}\")\n",
    "            \n",
    "    except Exception as e:\n",
    "        print(f\"保存文件时出错: {e}\")\n",
    "    \n",
    "    # 示例预测\n",
    "    valid_results = {k: v for k, v in results.items() if v is not None}\n",
    "    if valid_results:\n",
    "        best_model_name = max(valid_results.items(), key=lambda x: x[1]['auc'])[0]\n",
    "        best_model = valid_results[best_model_name]['model']\n",
    "        \n",
    "        print(f\"\\n使用最佳模型进行预测: {best_model_name}\")\n",
    "        \n",
    "        # 创建示例员工数据\n",
    "        example_employee = [30, 8000, 3, 4, 20, 5, 4, 1, 3, 2, 1][:len(feature_names)]\n",
    "        \n",
    "        turnover_prob = predict_employee_turnover(best_model, example_employee, feature_names, scaler)\n",
    "        risk_level = \"高风险\" if turnover_prob > 0.7 else \"中风险\" if turnover_prob > 0.3 else \"低风险\"\n",
    "        \n",
    "        print(f\"\\n示例员工离职风险评估:\")\n",
    "        print(f\"离职概率: {turnover_prob:.4f}\")\n",
    "        print(f\"风险等级: {risk_level}\")\n",
    "        print(f\"建议措施: {'立即干预' if turnover_prob > 0.7 else '重点关注' if turnover_prob > 0.3 else '正常关注'}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de097d55-b0a4-45da-8819-05d99a5199b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "案例5：肿瘤预测模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72904fc2-79ef-4052-990a-21f5dd677374",
   "metadata": {},
   "outputs": [],
   "source": [
    "案例背景\n",
    "医疗水平突飞猛进，人们对医院快速识别肿瘤是否为良性的要求同样也越来越高，\n",
    "能否根据患者肿瘤的相关特征水平快速判断肿瘤的性质影响着患者的治疗方式和痊愈速度。\n",
    "传统的做法是医生根据数十个指标来判断肿瘤的性质\n",
    "不过该方法的预测效果依赖于医生的个人经验而且效率较低，而通过机器学习我们有望能快速预测肿瘤的性质。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5758dced-a77f-4e0f-9281-7090f0e8617b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "import warnings\n",
    "\n",
    "# 忽略警告信息\n",
    "warnings.filterwarnings('ignore')\n",
    "# 设置中文显示\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 1. 数据加载\n",
    "data_path = r\"d:\\桌面\\商业数据分析\\@Python大数据分析与机器学习商业案例实战\\第6章 朴素贝叶斯模型\\源代码汇总_Jupyter Notebook格式（推荐）\\肿瘤数据.xlsx\"\n",
    "df = pd.read_excel(data_path)\n",
    "\n",
    "# 查看数据基本信息\n",
    "print(\"=\"*50)\n",
    "print(\"数据形状（行×列）：\", df.shape)\n",
    "print(\"\\n前5行数据：\")\n",
    "print(df.head())\n",
    "print(\"\\n数据类型与缺失值：\")\n",
    "print(df.info())\n",
    "print(\"\\n目标变量（肿瘤性质）分布：\")\n",
    "print(df['肿瘤性质'].value_counts())\n",
    "\n",
    "# 2. 数据预处理\n",
    "# 2.1 缺失值处理\n",
    "missing_rate = df.isnull().sum() / len(df) * 100\n",
    "print(\"=\"*50)\n",
    "print(\"各列缺失值比例（%）：\")\n",
    "print(missing_rate[missing_rate > 0].sort_values(ascending=False))\n",
    "\n",
    "for col in df.columns:\n",
    "    if df[col].dtype in ['float64', 'int64']:\n",
    "        df[col].fillna(df[col].mean(), inplace=True)\n",
    "    else:\n",
    "        df[col].fillna(df[col].mode()[0], inplace=True)\n",
    "\n",
    "# 2.2 标签编码\n",
    "le = LabelEncoder()\n",
    "df['肿瘤性质_编码'] = le.fit_transform(df['肿瘤性质'])\n",
    "print(\"=\"*50)\n",
    "print(\"肿瘤性质编码映射：\")\n",
    "for i, label in enumerate(le.classes_):\n",
    "    print(f\"{label} → {i}\")\n",
    "\n",
    "# 关键修正：将类别名称转换为字符串（解决len()错误）\n",
    "target_names = [str(label) for label in le.classes_]  # 转换为字符串列表\n",
    "\n",
    "# 2.3 特征与目标分离\n",
    "exclude_cols = ['患者ID', '肿瘤性质', '肿瘤性质_编码'] if '患者ID' in df.columns else ['肿瘤性质', '肿瘤性质_编码']\n",
    "feature_cols = [col for col in df.columns if col not in exclude_cols]\n",
    "X = df[feature_cols]\n",
    "y = df['肿瘤性质_编码']\n",
    "\n",
    "print(\"=\"*50)\n",
    "print(\"特征矩阵形状：\", X.shape)\n",
    "print(\"目标向量形状：\", y.shape)\n",
    "print(\"特征列名：\", feature_cols)\n",
    "\n",
    "# 2.4 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.3, random_state=42, stratify=y\n",
    ")\n",
    "\n",
    "print(\"=\"*50)\n",
    "print(f\"训练集：{X_train.shape[0]}条样本，测试集：{X_test.shape[0]}条样本\")\n",
    "print(\"训练集目标分布：\")\n",
    "print(y_train.value_counts(normalize=True).round(3) * 100)\n",
    "print(\"测试集目标分布：\")\n",
    "print(y_test.value_counts(normalize=True).round(3) * 100)\n",
    "\n",
    "# 2.5 特征标准化\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# 3. 模型训练与评估\n",
    "models = {\n",
    "    \"逻辑回归\": LogisticRegression(random_state=42, max_iter=1000),\n",
    "    \"朴素贝叶斯\": GaussianNB(),\n",
    "    \"决策树\": DecisionTreeClassifier(random_state=42, max_depth=10),\n",
    "    \"随机森林\": RandomForestClassifier(random_state=42, n_estimators=100),\n",
    "    \"SVM\": SVC(random_state=42, kernel='rbf')\n",
    "}\n",
    "\n",
    "model_results = {}\n",
    "\n",
    "for name, model in models.items():\n",
    "    print(\"=\"*50)\n",
    "    print(f\"===== {name} 训练与评估 =====\")\n",
    "    \n",
    "    # 选择数据（逻辑回归和SVM使用标准化数据）\n",
    "    if name in [\"逻辑回归\", \"SVM\"]:\n",
    "        X_tr, X_te = X_train_scaled, X_test_scaled\n",
    "    else:\n",
    "        X_tr, X_te = X_train, X_test\n",
    "    \n",
    "    # 训练模型\n",
    "    model.fit(X_tr, y_train)\n",
    "    \n",
    "    # 预测\n",
    "    y_train_pred = model.predict(X_tr)\n",
    "    y_test_pred = model.predict(X_te)\n",
    "    \n",
    "    # 计算指标\n",
    "    metrics = {\n",
    "        \"训练集准确率\": accuracy_score(y_train, y_train_pred),\n",
    "        \"测试集准确率\": accuracy_score(y_test, y_test_pred),\n",
    "        \"测试集精确率\": precision_score(y_test, y_test_pred, zero_division=0),\n",
    "        \"测试集召回率\": recall_score(y_test, y_test_pred, zero_division=0),\n",
    "        \"测试集F1值\": f1_score(y_test, y_test_pred, zero_division=0),\n",
    "        \"混淆矩阵\": confusion_matrix(y_test, y_test_pred)\n",
    "    }\n",
    "    model_results[name] = metrics\n",
    "    \n",
    "    # 输出评估结果\n",
    "    print(f\"\\n训练集准确率：{metrics['训练集准确率']:.4f}\")\n",
    "    print(f\"测试集准确率：{metrics['测试集准确率']:.4f}\")\n",
    "    print(f\"测试集精确率：{metrics['测试集精确率']:.4f}\")\n",
    "    print(f\"测试集召回率：{metrics['测试集召回率']:.4f}\")\n",
    "    print(f\"测试集F1值：{metrics['测试集F1值']:.4f}\")\n",
    "    print(\"\\n混淆矩阵：\")\n",
    "    print(metrics['混淆矩阵'])\n",
    "    print(\"\\n分类报告：\")\n",
    "    # 关键修正：使用转换后的字符串类型target_names\n",
    "    print(classification_report(y_test, y_test_pred, target_names=target_names, zero_division=0))\n",
    "\n",
    "# 4. 模型对比可视化\n",
    "comparison_df = pd.DataFrame({\n",
    "    \"准确率\": [model_results[name][\"测试集准确率\"] for name in models.keys()],\n",
    "    \"精确率\": [model_results[name][\"测试集精确率\"] for name in models.keys()],\n",
    "    \"召回率\": [model_results[name][\"测试集召回率\"] for name in models.keys()],\n",
    "    \"F1值\": [model_results[name][\"测试集F1值\"] for name in models.keys()]\n",
    "}, index=models.keys())\n",
    "\n",
    "print(\"=\"*50)\n",
    "print(\"五种模型测试集指标对比：\")\n",
    "print(comparison_df.round(4))\n",
    "\n",
    "# 绘制对比图\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "x = np.arange(len(models))\n",
    "width = 0.2\n",
    "\n",
    "rects1 = ax.bar(x - 1.5*width, comparison_df[\"准确率\"], width, label=\"准确率\")\n",
    "rects2 = ax.bar(x - 0.5*width, comparison_df[\"精确率\"], width, label=\"精确率\")\n",
    "rects3 = ax.bar(x + 0.5*width, comparison_df[\"召回率\"], width, label=\"召回率\")\n",
    "rects4 = ax.bar(x + 1.5*width, comparison_df[\"F1值\"], width, label=\"F1值\")\n",
    "\n",
    "ax.set_title(\"肿瘤预测模型性能对比\", fontsize=14)\n",
    "ax.set_xlabel(\"模型\", fontsize=12)\n",
    "ax.set_ylabel(\"指标值\", fontsize=12)\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(models.keys(), rotation=30)\n",
    "ax.legend()\n",
    "ax.set_ylim(0.8, 1.0)\n",
    "\n",
    "# 添加数值标签\n",
    "def add_bar_labels(rects):\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        ax.text(rect.get_x() + rect.get_width()/2., height + 0.005,\n",
    "                f'{height:.4f}', ha='center', va='bottom', fontsize=9)\n",
    "\n",
    "add_bar_labels(rects1)\n",
    "add_bar_labels(rects2)\n",
    "add_bar_labels(rects3)\n",
    "add_bar_labels(rects4)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 5. 最优模型推荐\n",
    "best_model = comparison_df[\"F1值\"].idxmax()\n",
    "print(\"=\"*50)\n",
    "print(f\"综合推荐最优模型：{best_model}\")\n",
    "print(f\"其F1值为：{comparison_df.loc[best_model, 'F1值']:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c7d4cce-cddf-442d-aa93-6cb6a687d328",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64fdf31d-5375-4c97-bdc9-99f02d3d6350",
   "metadata": {},
   "outputs": [],
   "source": []
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